{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "<!---------------------- Introduction Section ------------------->\n",
    "<h1> PTRAIL:  A <b><i>P</i></b>arallel\n",
    "<b><i>TR</i></b>ajectory\n",
    "d<b><i>A</i></b>ta\n",
    "preprocess<b><i>I</i></b>ng\n",
    "<b><i>L</i></b>ibrary\n",
    "</h1>\n",
    "\n",
    "<h2> Introduction </h2>\n",
    "\n",
    "<p align='justify'>\n",
    "PTRAIL is a state-of-the art Mobility Data Preprocessing Library that mainly deals with filtering data, generating features and interpolation of Trajectory Data.\n",
    "\n",
    "<b><i> The main features of PTRAIL are: </i></b>\n",
    "\n",
    "<ol align='justify'>\n",
    "<li> PTRAIL uses primarily parallel computation based on\n",
    "     python Pandas and numpy which makes it very fast as compared\n",
    "     to other libraries available.\n",
    "</li>\n",
    "\n",
    "<li> PTRAIL harnesses the full power of the machine that\n",
    "     it is running on by using all the cores available in the\n",
    "     computer.\n",
    "</li>\n",
    "\n",
    "<li> PTRAIL uses a customized DataFrame built on top of python\n",
    "     pandas for representation and storage of Trajectory Data.\n",
    "</li>\n",
    "\n",
    "<li> PTRAIL also provides several Temporal and kinematic features\n",
    "     which are calculated mostly using parallel computation for very\n",
    "     fast and accurate calculations.\n",
    "</li>\n",
    "\n",
    "<li> Moreover, PTRAIL also provides several filteration and\n",
    "     outlier detection methods for cleaning and noise reduction of\n",
    "     the Trajectory Data.\n",
    "</li>\n",
    "\n",
    "<li> Apart from the features mentioned above, <i><b> four </b></i>\n",
    "     different kinds of Trajectory Interpolation techniques are\n",
    "     offered by PTRAIL which is a first in the community.\n",
    "</li>\n",
    "</ol>\n",
    "</p>\n",
    "\n",
    "<!----------------- Dataset Link Section --------------------->\n",
    "<hr style=\"height:6px;background-color:black\">\n",
    "\n",
    "<p align='justify'>\n",
    "In the introduction of the library, the seagulls dataset is used\n",
    "which can be downloaded from the link below: <br>\n",
    "<span> &#8618; </span>\n",
    "<a href=\"https://github.com/YakshHaranwala/PTRAIL/blob/main/examples/data/gulls.csv\" target='_blank'> Seagulls Dataset </a>\n",
    "</p>\n",
    "\n",
    "<!----------------- NbViewer Link ---------------------------->\n",
    "<hr style=\"height:6px;background-color:black\">\n",
    "<p align='justify'>\n",
    "Note: Viewing this notebook in GitHub will not render JavaScript\n",
    "elements. Hence, for a better experience, click the link below\n",
    "to open the Jupyter notebook in NB viewer.\n",
    "\n",
    "<span> &#8618; </span>\n",
    "<a href=\"https://nbviewer.jupyter.org/github/YakshHaranwala/PTRAIL/blob/main/examples/0.%20Intro%20to%20PTRAIL.ipynb\" target='_blank'> Click Here </a>\n",
    "</p>\n",
    "\n",
    "<!------------------------- Documentation Link ----------------->\n",
    "<hr style=\"height:6px;background-color:black\">\n",
    "<p align='justify'>\n",
    "The Link to PTRAIL's Documentation is: <br>\n",
    "\n",
    "<span> &#8618; </span>\n",
    "<a href='https://PTRAIL.readthedocs.io/en/latest/' target='_blank'> <i> PTRAIL Documentation </i> </a>\n",
    "<hr style=\"height:6px;background-color:black\">\n",
    "</p>"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<h2> Importing Trajectory Data into a PTRAILDataFrame Dataframe </h2>\n",
    "\n",
    "<p align='justify'>\n",
    "PTRAIL Library stores Mobility Data (Trajectories) in a specialised\n",
    "pandas Dataframe structure called PTRAILDataFrame. As a result, the following\n",
    "constraints are enforced for the data to be able to be stores in a PTRAILDataFrame.\n",
    "\n",
    "<ol align='justify'>\n",
    "   <li>\n",
    "        Firstly, for a mobility dataset to be able to work with PTRAIL Library needs\n",
    "        to have the following mandatory columns present:\n",
    "       <ul type='square'>\n",
    "           <li> DateTime </li>\n",
    "           <li> Trajectory ID </li>\n",
    "           <li> Latitude </li>\n",
    "           <li> Longitude </li>\n",
    "       </ul>\n",
    "   </li>\n",
    "   <li>\n",
    "       Secondly, PTRAILDataFrame has a very specific constraint for the index of the\n",
    "       dataframes, the Library enforces a multi-index consisting of the\n",
    "       <b><i> Trajectory ID, DateTime </i></b> columns because the operations of the\n",
    "       library are dependent on the 2 columns. As a result, it is recommended\n",
    "       to not change the index and keep the multi-index of <b><i> Trajectory ID, DateTime </i></b>\n",
    "       at all times.\n",
    "   </li>\n",
    "   <li>\n",
    "        Note that since PTRAILDataFrame Dataframe is built on top of\n",
    "        python pandas, it does not have any restrictions on the number\n",
    "        of columns that the dataset has. The only requirement is that\n",
    "        the dataset should atleast contain the above mentioned four columns.\n",
    "   </li>\n",
    "</ol>\n",
    "</p>\n",
    "\n",
    "<hr style=\"height:6px;background-color:black\">"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dimensions of the dataframe:(6, 2)\n",
      "Type of the dataframe: <class 'ptrail.core.TrajectoryDF.PTRAILDataFrame'>\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "    METHOD - I:\n",
    "        1. Enter the trajectory data into a list.\n",
    "        2. Then, convert the list into a PTRAILDataFrame\n",
    "           Dataframe to be used with PTRAIL Library.\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "from ptrail.core.TrajectoryDF import PTRAILDataFrame\n",
    "\n",
    "list_data = [\n",
    "    [39.984094, 116.319236, '2008-10-23 05:53:05', 1],\n",
    "    [39.984198, 116.319322, '2008-10-23 05:53:06', 1],\n",
    "    [39.984224, 116.319402, '2008-10-23 05:53:11', 1],\n",
    "    [39.984224, 116.319404, '2008-10-23 05:53:11', 1],\n",
    "    [39.984224, 116.568956, '2008-10-23 05:53:11', 1],\n",
    "    [39.984224, 116.568956, '2008-10-23 05:53:11', 1]\n",
    "]\n",
    "list_df = PTRAILDataFrame(data_set=list_data,\n",
    "                        latitude='lat',\n",
    "                        longitude='lon',\n",
    "                        datetime='datetime',\n",
    "                        traj_id='id')\n",
    "print(f\"The dimensions of the dataframe:{list_df.shape}\")\n",
    "print(f\"Type of the dataframe: {type(list_df)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dimensions of the dataframe:(5, 2)\n",
      "Type of the dataframe: <class 'ptrail.core.TrajectoryDF.PTRAILDataFrame'>\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "    METHOD - II:\n",
    "        1. Enter the trajectory data into a dictionary.\n",
    "        2. Then, convert the dictionary into a PTRAILDataFrame\n",
    "           Dataframe to be used with PTRAIL Library.\n",
    "\"\"\"\n",
    "dict_data = {\n",
    "    'lat': [39.984198, 39.984224, 39.984094, 40.98, 41.256],\n",
    "    'lon': [116.319402, 116.319322, 116.319402, 116.3589, 117],\n",
    "    'datetime': ['2008-10-23 05:53:11', '2008-10-23 05:53:06', '2008-10-23 05:53:30', '2008-10-23 05:54:06', '2008-10-23 05:59:06'],\n",
    "    'id' : [1, 1, 1, 3, 3],\n",
    "}\n",
    "dict_df = PTRAILDataFrame(data_set=dict_data,\n",
    "                        latitude='lat',\n",
    "                        longitude='lon',\n",
    "                        datetime='datetime',\n",
    "                        traj_id='id')\n",
    "print(f\"The dimensions of the dataframe:{dict_df.shape}\")\n",
    "print(f\"Type of the dataframe: {type(dict_df)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "                                   lat         lon\ntraj_id DateTime                                  \n1       2008-10-23 05:53:05  39.984094  116.319236\n        2008-10-23 05:53:06  39.984198  116.319322\n        2008-10-23 05:53:11  39.984224  116.319402\n        2008-10-23 05:53:11  39.984224  116.319404\n        2008-10-23 05:53:11  39.984224  116.568956",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n    <tr>\n      <th>traj_id</th>\n      <th>DateTime</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">1</th>\n      <th>2008-10-23 05:53:05</th>\n      <td>39.984094</td>\n      <td>116.319236</td>\n    </tr>\n    <tr>\n      <th>2008-10-23 05:53:06</th>\n      <td>39.984198</td>\n      <td>116.319322</td>\n    </tr>\n    <tr>\n      <th>2008-10-23 05:53:11</th>\n      <td>39.984224</td>\n      <td>116.319402</td>\n    </tr>\n    <tr>\n      <th>2008-10-23 05:53:11</th>\n      <td>39.984224</td>\n      <td>116.319404</td>\n    </tr>\n    <tr>\n      <th>2008-10-23 05:53:11</th>\n      <td>39.984224</td>\n      <td>116.568956</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, printing the head of the dataframe with data\n",
    "# imported from a list.\n",
    "list_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "                                   lat         lon\ntraj_id DateTime                                  \n1       2008-10-23 05:53:06  39.984224  116.319322\n        2008-10-23 05:53:11  39.984198  116.319402\n        2008-10-23 05:53:30  39.984094  116.319402\n3       2008-10-23 05:54:06  40.980000  116.358900\n        2008-10-23 05:59:06  41.256000  117.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n    <tr>\n      <th>traj_id</th>\n      <th>DateTime</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">1</th>\n      <th>2008-10-23 05:53:06</th>\n      <td>39.984224</td>\n      <td>116.319322</td>\n    </tr>\n    <tr>\n      <th>2008-10-23 05:53:11</th>\n      <td>39.984198</td>\n      <td>116.319402</td>\n    </tr>\n    <tr>\n      <th>2008-10-23 05:53:30</th>\n      <td>39.984094</td>\n      <td>116.319402</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">3</th>\n      <th>2008-10-23 05:54:06</th>\n      <td>40.980000</td>\n      <td>116.358900</td>\n    </tr>\n    <tr>\n      <th>2008-10-23 05:59:06</th>\n      <td>41.256000</td>\n      <td>117.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, printing the head of the dataframe with data\n",
    "# imported from a dictionary.\n",
    "dict_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "\"\"\"\n",
    "    METHOD - III:\n",
    "        1. First, import the seagulls dataset from the csv file\n",
    "           using pandas into a pandas dataframe.\n",
    "        2. Then, convert the pandas dataframe into a PTRAILDataFrame\n",
    "           DataFrame to be used with PTRAIL library.\n",
    "\"\"\"\n",
    "df = pd.read_csv('./data/gulls.csv')\n",
    "seagulls_df = PTRAILDataFrame(data_set=df,\n",
    "                            latitude='location-lat',\n",
    "                            longitude='location-long',\n",
    "                            datetime='timestamp',\n",
    "                            traj_id='tag-local-identifier',\n",
    "                            rest_of_columns=[])\n",
    "print(f\"The dimensions of the dataframe:{seagulls_df.shape}\")\n",
    "print(f\"Type of the dataframe: {type(seagulls_df)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dimensions of the dataframe:(89869, 8)\n",
      "Type of the dataframe: <class 'ptrail.core.TrajectoryDF.PTRAILDataFrame'>\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "                               event-id  visible       lon       lat  \\\ntraj_id DateTime                                                       \n91732   2009-05-27 14:00:00  1082620685     True  24.58617  61.24783   \n        2009-05-27 20:00:00  1082620686     True  24.58217  61.23267   \n        2009-05-28 05:00:00  1082620687     True  24.53133  61.18833   \n        2009-05-28 08:00:00  1082620688     True  24.58200  61.23283   \n        2009-05-28 14:00:00  1082620689     True  24.58250  61.23267   \n\n                            sensor-type individual-taxon-canonical-name  \\\ntraj_id DateTime                                                          \n91732   2009-05-27 14:00:00         gps                    Larus fuscus   \n        2009-05-27 20:00:00         gps                    Larus fuscus   \n        2009-05-28 05:00:00         gps                    Larus fuscus   \n        2009-05-28 08:00:00         gps                    Larus fuscus   \n        2009-05-28 14:00:00         gps                    Larus fuscus   \n\n                            individual-local-identifier  \\\ntraj_id DateTime                                          \n91732   2009-05-27 14:00:00                      91732A   \n        2009-05-27 20:00:00                      91732A   \n        2009-05-28 05:00:00                      91732A   \n        2009-05-28 08:00:00                      91732A   \n        2009-05-28 14:00:00                      91732A   \n\n                                                                    study-name  \ntraj_id DateTime                                                                \n91732   2009-05-27 14:00:00  Navigation experiments in lesser black-backed ...  \n        2009-05-27 20:00:00  Navigation experiments in lesser black-backed ...  \n        2009-05-28 05:00:00  Navigation experiments in lesser black-backed ...  \n        2009-05-28 08:00:00  Navigation experiments in lesser black-backed ...  \n        2009-05-28 14:00:00  Navigation experiments in lesser black-backed ...  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>event-id</th>\n      <th>visible</th>\n      <th>lon</th>\n      <th>lat</th>\n      <th>sensor-type</th>\n      <th>individual-taxon-canonical-name</th>\n      <th>individual-local-identifier</th>\n      <th>study-name</th>\n    </tr>\n    <tr>\n      <th>traj_id</th>\n      <th>DateTime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">91732</th>\n      <th>2009-05-27 14:00:00</th>\n      <td>1082620685</td>\n      <td>True</td>\n      <td>24.58617</td>\n      <td>61.24783</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n    <tr>\n      <th>2009-05-27 20:00:00</th>\n      <td>1082620686</td>\n      <td>True</td>\n      <td>24.58217</td>\n      <td>61.23267</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 05:00:00</th>\n      <td>1082620687</td>\n      <td>True</td>\n      <td>24.53133</td>\n      <td>61.18833</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 08:00:00</th>\n      <td>1082620688</td>\n      <td>True</td>\n      <td>24.58200</td>\n      <td>61.23283</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 14:00:00</th>\n      <td>1082620689</td>\n      <td>True</td>\n      <td>24.58250</td>\n      <td>61.23267</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, print the head of the seagulls_df dataframe.\n",
    "seagulls_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<h1>Trajectory Feature Extraction </h1>\n",
    "\n",
    "<p align='justify'>\n",
    "As mentioned above, PTRAIL offers a multitude of features\n",
    "which are calculated based on both Datetime, and the coordinates\n",
    "of the points given in the data. Both the feature module are named\n",
    "as follows:\n",
    "</p>\n",
    "\n",
    "<ul align='justify'>\n",
    "    <li> temporal_features (based on DateTime) </li>\n",
    "    <li> kinematic_features (based on geographical coordinates) </li>\n",
    "</ul>\n",
    "<hr style=\"background-color:black; height:7px\">"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<h2> PTRAIL Temporal Features </h2>\n",
    "\n",
    "<p align='justify'>\n",
    "\n",
    "The following steps are performed to demonstrate the usage of\n",
    "Temporal features present in PTRAIL:\n",
    "\n",
    "<ul type='square', align='justify'>\n",
    "<li>Various features Date, Time, Week-day, Time of Day etc are\n",
    "    calculated using temporal_features.py module functions and\n",
    "    the results are appended to the original dataframe.\n",
    "</li>\n",
    "<li> Not all the functions present in the module are demonstrated\n",
    "     here. Only a few of the functions are demonstrated here, keeping\n",
    "     the length of jupyter notebook in mind. Further functions can\n",
    "     be explored in the documentation of the library. The documentation\n",
    "     link is provided in the introduction section of this notebook.\n",
    "</li>\n",
    "</ul>\n",
    "</p>"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 257 ms, sys: 16.1 ms, total: 273 ms\n",
      "Wall time: 272 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": "                               event-id  visible       lon       lat  \\\ntraj_id DateTime                                                       \n91732   2009-05-27 14:00:00  1082620685     True  24.58617  61.24783   \n        2009-05-27 20:00:00  1082620686     True  24.58217  61.23267   \n        2009-05-28 05:00:00  1082620687     True  24.53133  61.18833   \n        2009-05-28 08:00:00  1082620688     True  24.58200  61.23283   \n        2009-05-28 14:00:00  1082620689     True  24.58250  61.23267   \n\n                            sensor-type individual-taxon-canonical-name  \\\ntraj_id DateTime                                                          \n91732   2009-05-27 14:00:00         gps                    Larus fuscus   \n        2009-05-27 20:00:00         gps                    Larus fuscus   \n        2009-05-28 05:00:00         gps                    Larus fuscus   \n        2009-05-28 08:00:00         gps                    Larus fuscus   \n        2009-05-28 14:00:00         gps                    Larus fuscus   \n\n                            individual-local-identifier  \\\ntraj_id DateTime                                          \n91732   2009-05-27 14:00:00                      91732A   \n        2009-05-27 20:00:00                      91732A   \n        2009-05-28 05:00:00                      91732A   \n        2009-05-28 08:00:00                      91732A   \n        2009-05-28 14:00:00                      91732A   \n\n                                                                    study-name  \\\ntraj_id DateTime                                                                 \n91732   2009-05-27 14:00:00  Navigation experiments in lesser black-backed ...   \n        2009-05-27 20:00:00  Navigation experiments in lesser black-backed ...   \n        2009-05-28 05:00:00  Navigation experiments in lesser black-backed ...   \n        2009-05-28 08:00:00  Navigation experiments in lesser black-backed ...   \n        2009-05-28 14:00:00  Navigation experiments in lesser black-backed ...   \n\n                                   Date Day_Of_Week    Time_Of_Day  \ntraj_id DateTime                                                    \n91732   2009-05-27 14:00:00  2009-05-27   Wednesday           Noon  \n        2009-05-27 20:00:00  2009-05-27   Wednesday        Evening  \n        2009-05-28 05:00:00  2009-05-28    Thursday  Early Morning  \n        2009-05-28 08:00:00  2009-05-28    Thursday  Early Morning  \n        2009-05-28 14:00:00  2009-05-28    Thursday           Noon  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>event-id</th>\n      <th>visible</th>\n      <th>lon</th>\n      <th>lat</th>\n      <th>sensor-type</th>\n      <th>individual-taxon-canonical-name</th>\n      <th>individual-local-identifier</th>\n      <th>study-name</th>\n      <th>Date</th>\n      <th>Day_Of_Week</th>\n      <th>Time_Of_Day</th>\n    </tr>\n    <tr>\n      <th>traj_id</th>\n      <th>DateTime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">91732</th>\n      <th>2009-05-27 14:00:00</th>\n      <td>1082620685</td>\n      <td>True</td>\n      <td>24.58617</td>\n      <td>61.24783</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n      <td>2009-05-27</td>\n      <td>Wednesday</td>\n      <td>Noon</td>\n    </tr>\n    <tr>\n      <th>2009-05-27 20:00:00</th>\n      <td>1082620686</td>\n      <td>True</td>\n      <td>24.58217</td>\n      <td>61.23267</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n      <td>2009-05-27</td>\n      <td>Wednesday</td>\n      <td>Evening</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 05:00:00</th>\n      <td>1082620687</td>\n      <td>True</td>\n      <td>24.53133</td>\n      <td>61.18833</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n      <td>2009-05-28</td>\n      <td>Thursday</td>\n      <td>Early Morning</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 08:00:00</th>\n      <td>1082620688</td>\n      <td>True</td>\n      <td>24.58200</td>\n      <td>61.23283</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n      <td>2009-05-28</td>\n      <td>Thursday</td>\n      <td>Early Morning</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 14:00:00</th>\n      <td>1082620689</td>\n      <td>True</td>\n      <td>24.58250</td>\n      <td>61.23267</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n      <td>2009-05-28</td>\n      <td>Thursday</td>\n      <td>Noon</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\"\"\"\n",
    "    To demonstrate the temporal features, we will:\n",
    "        1. First, import the temporal_features.py module from the\n",
    "           features package.\n",
    "        2. Generate Date, Day_Of_Week, Time_Of_day features on\n",
    "           the seagulls dataset.\n",
    "        3. Print the execution time of the code.\n",
    "        4. Finally, check the head of the dataframe to\n",
    "           see the results of feature generation.\n",
    "\"\"\"\n",
    "from ptrail.features.temporal_features import TemporalFeatures as temporal\n",
    "\n",
    "temporal_features_df = temporal.create_date_column(seagulls_df)\n",
    "temporal_features_df = temporal.create_day_of_week_column(temporal_features_df)\n",
    "temporal_features_df = temporal.create_time_of_day_column(temporal_features_df)\n",
    "temporal_features_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<h2> PTRAIL kinematic Features </h2>\n",
    "\n",
    "<p align='justify'>\n",
    "\n",
    "The following steps are performed to demonstrate the usage of\n",
    "kinematic features present in PTRAIL:\n",
    "</p>\n",
    "<ul type='square', align='justify'>\n",
    "<li>Various features like Distance, Jerk and rate of bearing rate are\n",
    "    calculated using kinematic_features.py module functions and\n",
    "    the results are appended to the original dataframe.\n",
    "</li>\n",
    "<li> While calculating jerk, the columns of acceleration, speed and\n",
    "     distance all are added to the dataframe. Similarly, when calculating\n",
    "     rate of bearing rate, the column of Bearing and Bearing rate are\n",
    "     added to the dataframe.\n",
    "</li>\n",
    "<li> Not all the functions present in the module are demonstrated\n",
    "     here. Only a few of the functions are demonstrated here, keeping\n",
    "     the length of jupyter notebook in mind. Further functions can\n",
    "     be explored in the documentation of the library. The documentation\n",
    "     link is provided in the introduction section of this notebook.\n",
    "</li>\n",
    "</ul>"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 614 ms, sys: 164 ms, total: 777 ms\n",
      "Wall time: 1.16 s\n"
     ]
    },
    {
     "data": {
      "text/plain": "                               event-id  visible       lon       lat  \\\ntraj_id DateTime                                                       \n91732   2009-05-27 14:00:00  1082620685     True  24.58617  61.24783   \n        2009-05-27 20:00:00  1082620686     True  24.58217  61.23267   \n        2009-05-28 05:00:00  1082620687     True  24.53133  61.18833   \n        2009-05-28 08:00:00  1082620688     True  24.58200  61.23283   \n        2009-05-28 14:00:00  1082620689     True  24.58250  61.23267   \n\n                            sensor-type individual-taxon-canonical-name  \\\ntraj_id DateTime                                                          \n91732   2009-05-27 14:00:00         gps                    Larus fuscus   \n        2009-05-27 20:00:00         gps                    Larus fuscus   \n        2009-05-28 05:00:00         gps                    Larus fuscus   \n        2009-05-28 08:00:00         gps                    Larus fuscus   \n        2009-05-28 14:00:00         gps                    Larus fuscus   \n\n                            individual-local-identifier  \\\ntraj_id DateTime                                          \n91732   2009-05-27 14:00:00                      91732A   \n        2009-05-27 20:00:00                      91732A   \n        2009-05-28 05:00:00                      91732A   \n        2009-05-28 08:00:00                      91732A   \n        2009-05-28 14:00:00                      91732A   \n\n                                                                    study-name  \\\ntraj_id DateTime                                                                 \n91732   2009-05-27 14:00:00  Navigation experiments in lesser black-backed ...   \n        2009-05-27 20:00:00  Navigation experiments in lesser black-backed ...   \n        2009-05-28 05:00:00  Navigation experiments in lesser black-backed ...   \n        2009-05-28 08:00:00  Navigation experiments in lesser black-backed ...   \n        2009-05-28 14:00:00  Navigation experiments in lesser black-backed ...   \n\n                                Distance     Speed  Acceleration  \\\ntraj_id DateTime                                                   \n91732   2009-05-27 14:00:00          NaN       NaN           NaN   \n        2009-05-27 20:00:00  1699.244398  0.078669           NaN   \n        2009-05-28 05:00:00  5632.120064  0.173831      0.000003   \n        2009-05-28 08:00:00  5643.314949  0.522529      0.000032   \n        2009-05-28 14:00:00    32.131494  0.001488     -0.000024   \n\n                                     Jerk     Bearing  Bearing_Rate  \\\ntraj_id DateTime                                                      \n91732   2009-05-27 14:00:00           NaN         NaN           NaN   \n        2009-05-27 20:00:00           NaN  187.236713           NaN   \n        2009-05-28 05:00:00           NaN  208.929407      0.000670   \n        2009-05-28 08:00:00  2.717572e-09   28.716637     -0.016686   \n        2009-05-28 14:00:00 -2.611536e-09  123.620959      0.004394   \n\n                             Rate_of_bearing_rate  \ntraj_id DateTime                                   \n91732   2009-05-27 14:00:00                   NaN  \n        2009-05-27 20:00:00                   NaN  \n        2009-05-28 05:00:00              0.000670  \n        2009-05-28 08:00:00             -0.016686  \n        2009-05-28 14:00:00              0.004394  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>event-id</th>\n      <th>visible</th>\n      <th>lon</th>\n      <th>lat</th>\n      <th>sensor-type</th>\n      <th>individual-taxon-canonical-name</th>\n      <th>individual-local-identifier</th>\n      <th>study-name</th>\n      <th>Distance</th>\n      <th>Speed</th>\n      <th>Acceleration</th>\n      <th>Jerk</th>\n      <th>Bearing</th>\n      <th>Bearing_Rate</th>\n      <th>Rate_of_bearing_rate</th>\n    </tr>\n    <tr>\n      <th>traj_id</th>\n      <th>DateTime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">91732</th>\n      <th>2009-05-27 14:00:00</th>\n      <td>1082620685</td>\n      <td>True</td>\n      <td>24.58617</td>\n      <td>61.24783</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2009-05-27 20:00:00</th>\n      <td>1082620686</td>\n      <td>True</td>\n      <td>24.58217</td>\n      <td>61.23267</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n      <td>1699.244398</td>\n      <td>0.078669</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>187.236713</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 05:00:00</th>\n      <td>1082620687</td>\n      <td>True</td>\n      <td>24.53133</td>\n      <td>61.18833</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n      <td>5632.120064</td>\n      <td>0.173831</td>\n      <td>0.000003</td>\n      <td>NaN</td>\n      <td>208.929407</td>\n      <td>0.000670</td>\n      <td>0.000670</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 08:00:00</th>\n      <td>1082620688</td>\n      <td>True</td>\n      <td>24.58200</td>\n      <td>61.23283</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n      <td>5643.314949</td>\n      <td>0.522529</td>\n      <td>0.000032</td>\n      <td>2.717572e-09</td>\n      <td>28.716637</td>\n      <td>-0.016686</td>\n      <td>-0.016686</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 14:00:00</th>\n      <td>1082620689</td>\n      <td>True</td>\n      <td>24.58250</td>\n      <td>61.23267</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n      <td>32.131494</td>\n      <td>0.001488</td>\n      <td>-0.000024</td>\n      <td>-2.611536e-09</td>\n      <td>123.620959</td>\n      <td>0.004394</td>\n      <td>0.004394</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\"\"\"\n",
    "    To demonstrate the kinematic features, we will:\n",
    "        1. First, import the kinematic_features.py module from the\n",
    "           features package.\n",
    "        2. Calculate Distance, Jerk and Rate of bearing rate features on\n",
    "           the seagulls dataset.\n",
    "        3. Print the execution time of the code.\n",
    "        4. Finally, check the head of the dataframe to\n",
    "           see the results of feature generation.\n",
    "\"\"\"\n",
    "\n",
    "from ptrail.features.kinematic_features import KinematicFeatures as kinematic\n",
    "\n",
    "kinematic_features_df = kinematic.create_distance_column(seagulls_df)\n",
    "kinematic_features_df = kinematic.create_jerk_column(kinematic_features_df)\n",
    "kinematic_features_df = kinematic.create_rate_of_br_column(kinematic_features_df)\n",
    "kinematic_features_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<h1> Trajectory Data Preprocessing </h1>\n",
    "\n",
    "<p align = 'justify'>\n",
    "In the form of preprocessing PTRAIL provides:\n",
    "</p>\n",
    "<ul align = 'justify'>\n",
    "<li> Outlier Detection </li>\n",
    "<li> Data filtering based on various constraints </li>\n",
    "<li> Trajectory Interpolation </li>\n",
    "</ul>\n",
    "<p> For interpolation the user needs to provide the type of interpolation. </p>\n",
    "\n",
    "<hr style=\"background-color:black; height:7px\">"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<h2> Outlier Detection and Data Filtering </h2>\n",
    "\n",
    "<p> PTRAIL provides several outlier detection method based on: </p>\n",
    "<ul align='justify'>\n",
    "<li>Distance</li>\n",
    "<li> Speed </li>\n",
    "<li> Trajectory length </li>\n",
    "<li> Hampel filter algorithm </li>\n",
    "</ul>\n",
    "<p> It also provides several filtering methods based on various constraints such as: </p>\n",
    "<ul type = 'square' align='justify'>\n",
    "<li> Date </li>\n",
    "<li> Speed </li>\n",
    "<li> Distance, etc. </li>\n",
    "</ul>\n",
    "<p align = 'justify'> Not all the functions present in the module are demonstrated\n",
    "     here. Only a few of the functions are demonstrated here, keeping\n",
    "     the length of jupyter notebook in mind. Further functions can\n",
    "     be explored in the documentation of the library. The documentation\n",
    "     link is provided in the introduction section of this notebook.\n",
    "</p>"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of original: 89869\n",
      "Length after outlier removal: 74765\n",
      "Number of points removed: 15104\n",
      "CPU times: user 136 ms, sys: 104 ms, total: 240 ms\n",
      "Wall time: 7.64 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yjharanwala/Desktop/PTRAIL/ptrail/preprocessing/filters.py:762: UserWarning: If kinematic features have been generated on the dataframe, then make sure to generate them again as outlier detection drops the point from the dataframe and does not run the kinematic features again.\n",
      "  warnings.warn(\"If kinematic features have been generated on the dataframe, then make \"\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\"\"\"\n",
    "    To demonstrate the kinematic features, we will:\n",
    "        1. First, import the filters.py module from the\n",
    "           preprocessing package.\n",
    "        2. Detect outliers and Filter out data based on \n",
    "            bounding box, date and distance.\n",
    "        3. Print the execution time of the code.\n",
    "\"\"\"\n",
    "from ptrail.preprocessing.filters import Filters as filt\n",
    "\n",
    "# Makes use of hampel filter from preprocessing package for outlier removal\n",
    "outlier_df = filt.hampel_outlier_detection(seagulls_df, column_name='lat')\n",
    "print(f\"Length of original: {len(seagulls_df)}\")\n",
    "print(f\"Length after outlier removal: {len(outlier_df)}\")\n",
    "print(f\"Number of points removed: {len(seagulls_df) - len(outlier_df)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of original: 89869\n",
      "Length of data in the bounding box: 10178\n",
      "Number of points removed: 79691\n",
      "CPU times: user 14.9 ms, sys: 439 µs, total: 15.3 ms\n",
      "Wall time: 14 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Filters and gives out data contained within the given bounding box\n",
    "filter_df_bbox = filt.filter_by_bounding_box(seagulls_df, (61, 24, 65, 25))\n",
    "print(f\"Length of original: {len(seagulls_df)}\")\n",
    "print(f\"Length of data in the bounding box: {len(filter_df_bbox)}\")\n",
    "print(f\"Number of points removed: {len(seagulls_df) - len(filter_df_bbox)}\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of original: 89869\n",
      "Length of data within specified date: 2692\n",
      "Number of points removed: 87177\n",
      "CPU times: user 57.8 ms, sys: 3.87 ms, total: 61.7 ms\n",
      "Wall time: 59.7 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Gives out the points contained within the given date range\n",
    "filter_df_date = filt.filter_by_date(temporal_features_df, start_date='2009-05-28', end_date='2009-06-30')\n",
    "print(f\"Length of original: {len(temporal_features_df)}\")\n",
    "print(f\"Length of data within specified date: {len(filter_df_date)}\")\n",
    "print(f\"Number of points removed: {len(temporal_features_df) - len(filter_df_date)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of original: 89869\n",
      "Length of Max distance Filtered DF: 47750\n",
      "Number of points removed: 42119\n",
      "CPU times: user 45.5 ms, sys: 215 µs, total: 45.7 ms\n",
      "Wall time: 43.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Filtered dataset with a given maximum distance \n",
    "filter_df_distance = filt.filter_by_max_consecutive_distance(kinematic_features_df, max_distance=4500)\n",
    "print(f\"Length of original: {len(kinematic_features_df)}\")\n",
    "print(f\"Length of Max distance Filtered DF: {len(filter_df_distance)}\")\n",
    "print(f\"Number of points removed: {len(kinematic_features_df) - len(filter_df_distance)}\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<h2> Trajectory Interpolation </h2>\n",
    "\n",
    "<p align='justify'>\n",
    "As mentioned above, for the first time in the community, PTRAIL\n",
    "offers <b><i> four different Interpolation Techniques </i></b> built\n",
    "into it.\n",
    "\n",
    "Trajectory Interpolation is widely used when the trajectory data\n",
    "on hand is not clean and has several Jumps in it which makes the\n",
    "trajectory abrupt. Using interpolation techniques, those jumps\n",
    "between the trajectory points can be filled in order to make the\n",
    "trajectory smoother.\n",
    "\n",
    "PTRAIL offers  following four interpolation techniques:\n",
    "</p>\n",
    "<ol align='justify'>\n",
    "<i>\n",
    "<li> Linear Interpolation </li>\n",
    "<li> Cubic Interpolation </li>\n",
    "<li> Random-Walk Interpolation </li>\n",
    "<li> Kinematic Interpolation </li>\n",
    "</i>\n",
    "</ol>\n",
    "\n",
    "In the examples of interpolation provided in this notebook, the\n",
    "examples are demonstrated on a single trajectory selected from the\n",
    "seagulls dataset. However, it is to be noted that the interpolation\n",
    "works equally well on datasets containing several trajectories.\n",
    "\n",
    "<h3> Note </h3>\n",
    "<p align='justify'>\n",
    "Furthermore, it is also worthwhile to note that PTRAIL only\n",
    "interpolates 4 fundamental columns i.e., <i><b> Latitude, Longitude,\n",
    "DateTime and Trajectory ID </b></i>. Hence, the dataframe returned\n",
    "by the interpolation methods only contain the above mentioned 4\n",
    "columns. This decision is taken while keeping the following point\n",
    "in mind that it is not possible to interpolate other kinds of\n",
    "data that may or may not be present in the dataset.\n",
    "</p>"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True     1501\n",
      "False     469\n",
      "Name: DateTime, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "    First, the following operations are performed:\n",
    "        1. Import the necessary interpolation modules\n",
    "           from the preprocessing package.\n",
    "        2. Select a single trajectory ID from the seagulls\n",
    "           dataset and then plot it using folium.\n",
    "        3. The number of points having time jump greater than\n",
    "           4 hours between 2 points is also calculated and shown.\n",
    "\"\"\"\n",
    "import ptrail.utilities.constants as const\n",
    "import folium\n",
    "from ptrail.preprocessing.interpolation import Interpolation as ip\n",
    "\n",
    "small_seagulls = seagulls_df.reset_index().loc[seagulls_df.reset_index()[const.TRAJECTORY_ID] == '91732'][[const.TRAJECTORY_ID, const.DateTime, const.LAT, const.LONG]]\n",
    "time_del = small_seagulls.reset_index()[const.DateTime].diff().dt.total_seconds()\n",
    "print((time_del > 3600*4).value_counts())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here, we plot the smaller trajectory on a folium map.\n",
    "sw = small_seagulls[['lat', 'lon']].min().values.tolist()\n",
    "ne = small_seagulls[['lat', 'lon']].max().values.tolist()\n",
    "coords = [zip(small_seagulls[const.LAT], small_seagulls[const.LONG])]\n",
    "m1 = folium.Map(location=[small_seagulls[const.LAT].iloc[0], small_seagulls[const.LONG].iloc[0]], zoom_start=1000)\n",
    "\n",
    "folium.PolyLine(coords,\n",
    "                color='blue',\n",
    "                weight=2,\n",
    "                opacity=0.7).add_to(m1)\n",
    "m1.fit_bounds([sw, ne])\n",
    "m1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DF Length: 1970\n",
      "Interpolated DF Length: 3471\n",
      "CPU times: user 18.1 ms, sys: 20.4 ms, total: 38.4 ms\n",
      "Wall time: 4.46 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\"\"\"\n",
    "    Now, to demonstrate interpolation, the following steps\n",
    "    are taken:\n",
    "        1. Interpolate the selected trajectory using all of\n",
    "           the above mentioned techniques and print their\n",
    "           execution times along with them.\n",
    "        2. Then, plot all the trajectories side by side along\n",
    "           with the original trajectory on a scatter plot to\n",
    "           see how the time jumps are filled and the points\n",
    "           are inserted into the trajectory.\n",
    "\"\"\"\n",
    "\n",
    "# First, linear interpolation is performed.\n",
    "linear_ip_gulls = ip.interpolate_position(dataframe=small_seagulls,\n",
    "                                          time_jump=3600*4,\n",
    "                                          ip_type='linear')\n",
    "print(f\"Original DF Length: {len(small_seagulls)}\")\n",
    "print(f\"Interpolated DF Length: {len(linear_ip_gulls)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DF Length: 1970\n",
      "Interpolated DF Length: 3471\n",
      "CPU times: user 13.4 ms, sys: 20.3 ms, total: 33.6 ms\n",
      "Wall time: 4.36 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Now, cubic interpolation is performed.\n",
    "cubic_ip_gulls = ip.interpolate_position(dataframe=small_seagulls,\n",
    "                                         time_jump=3600*4,\n",
    "                                         ip_type='cubic')\n",
    "print(f\"Original DF Length: {len(small_seagulls)}\")\n",
    "print(f\"Interpolated DF Length: {len(cubic_ip_gulls)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DF Length: 1970\n",
      "Interpolated DF Length: 3471\n",
      "CPU times: user 22 ms, sys: 15.7 ms, total: 37.7 ms\n",
      "Wall time: 4.38 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Now, random-walk interpolation is performed.\n",
    "rw_ip_gulls = ip.interpolate_position(dataframe=small_seagulls,\n",
    "                                      time_jump=3600*4,\n",
    "                                      ip_type='random-walk')\n",
    "print(f\"Original DF Length: {len(small_seagulls)}\")\n",
    "print(f\"Interpolated DF Length: {len(rw_ip_gulls)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DF Length: 1970\n",
      "Interpolated DF Length: 3470\n",
      "CPU times: user 23.7 ms, sys: 20 ms, total: 43.7 ms\n",
      "Wall time: 4.15 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Now, kinematic interpolation is performed.\n",
    "kin_ip_gulls = ip.interpolate_position(dataframe=small_seagulls,\n",
    "                                       time_jump=3600*4,\n",
    "                                       ip_type='kinematic')\n",
    "print(f\"Original DF Length: {len(small_seagulls)}\")\n",
    "print(f\"Interpolated DF Length: {len(kin_ip_gulls)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
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      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1800x1440 with 4 Axes>",
      "image/png": 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K/JMNL31TzqUR3hf/BVxWdNeTCbhszhjzDKC4tvWDwN9ba6dCtv8Q7vLV4r/jXcaYoNrrsVhrd8Rc5Z9xlyJ74r6H6qKJx3aFtfb2Mst8AlcGxlPP95qIiCTHASW3H2/KKBpji7X2k2ELFL5LFJcJ6MTN9WrhQyW332ut/VmUFa2109bav9RoHLVQ0+9l1TLGnAD0F931jcJ3qlDWlbcrfl6eb4x5YYxdf9Rauy3G8qX2AW+11o4HLWCtfRx4Gy4hx/NOY0xQid33l9wetNb+b9ggrLWfx12p6ukGzg9bJypr7SPW2rEYy9+BuyLV83xjzFG1GEsNNOPY3gxcaK21QQtYVzrzi0V3zcOVThEJpEC3SDKsi7jc5qJ/Zwiuj3Y2M7WNwWWwRKrBZ639Pe5sq+dVEccW1feL/t1XqB0d1ZYqJ1yN8B0brf7jNbiguOeEegym0EjytUV3fdNae1uUdQuvmS8V3fWyQmOnKP4zLNgf0b9FXK70pMFbApYrvf/ThQl2KGvt73B1Cj0HAq+MOLaastbuxdW/9FTU6LMOmnFs7wJCv4gW9vE4rjyM59nGmKBmNyIi0jr2L7ndyoHuj0dcbi0uk9sTNGeKzBjTh2to77nBWvu1arfbRLX+XlatC0pufzTGul/GZeZ6on6vGwP+M8Z+/HynENgNVQjIF88FFwMvK12ucCLhdUV3PYorCRPFR0puN7P56fdLbjd9Lt/EY/sZa+1E+cVmvdegTt+bpXUo0C3SfCPW2j9HXLY0c/HggOVOKfq3xZUQiOMXRf/uK2SIR2aM6TDG7GeMOdIYc1TxD7ODu93AETE2/cM442iSn5RfBKy1k7hmG56g57JaJzP7s/6ygOWCFL8Wuoje8LPa5+p6a+328ov99eRM8XvjecaYeT6L/m3J7W/FGM83y2yrpowxXcaYA4wxS33eQ8WZMcfWuoN8hZpxbH8a44qB4tfHAlw9fxERaS+BWYMp9xBwbZQFC1dFFmdaLzbGLK1y/6VByQ1Vbq+Z6vG9rFrF3+tuiDE+L2nld0V3vSjiqteEZWJHVDYZochQyW2/zPO/YXZz2U2FBJCyCsH0W4vuelah8WJdGKfbGHO4zzy+q2Txp9VrHDE069hG+t5M495r0iKCLgkRkcaJlF1bUJqJUtp121M8ibkf2N8YU5rVEqb4zOp83Jn1O4MWNsb0AGfhzgSfADyVmQ7J5eyP68IexY0Rl2umSp/PoOeyWqUT2kdjXiJXmnH/VFyT0zBTuEvRqvG78ovMWd6bKM4HngHcULLM84r+fb+19p4Y2/91yLaqZow5EHg9LtPmBGa63JeTwb12HqvleCrQjGNb7WdntVcciIhIsj1acvvJTRlF/W2LWSrud8y+eup5QKxyhCVKM1Kvq2JbzVaP72UVM8YcAhxTdNedFZS6KJ7vPDXiOjfG3IefOHP50mX95oKl9/0m3nD4NXB84d8Z4ERq+Fo1xrwIeCOurMYzgIURV43zHb1emnFsn7DWjkbcft3fa9JaFOgWab44l1GWXtpTekbYU5wlHRqkjuiAoG0YY96Ou8Sv0jOrcf6jSlL9viCVPp9Bz2W1SjPmf+G7VHSl9S79PBbxMrQwf4q5fGn29yHFNwpXJRRn8Y7E2bi19h5jzG5mJq01ySQoZGN/AHcZaqVZxk0NdDfx2Nbjs1NERFpHaaB7v2YMogFqOmeqwGElt+MEi5MmaXOL0nn82YWfSkWZx0P137l2lesHVOJOXM8W7ypFv9dk6fww1nwT+GOZ7VXEGPN0XJmXF1e4iSQEbZtxbCO/16y1EyVVBzWPl1AKdIs0XzXN+uYoNO+o9aVYvgE4Y8wg8erE+YlTdiFys48mqunzWQNRJ7RRRQnG1uJ5eiLm8qWTpf1KbpdmS8TdvrcPLxhbdfZFoU70V4C3V7mpZpcuadaxTdp7TUREkqU0W/C4poyi/mo9Z4rrwKJ/jxfK86VV0uYWzZjHQ/Vz+VivSWutNcY8wcxrcT+fxUrvq/Z1X4u5/HOBq6vcVrPn8dCcY5u095q0kCS8qUSktupxhnNOGRJjzEuZG+T+NbAKeAnwFFzAfZ611ng/wN/XYXwSrNavh0Y18YtbRzPuuCqp01nrv/1tzA5yW+Aq4D242oRH4t5DnSXvocHSDSVMEo6tiIhIXcuOJUi950xxtGod9GZJa+ZqI+aCTX3dF/oBfYvZQd2dwKeAM3HlSw4AFhbP4wtz+aRL0meKSGzK6BZpMdba3SVlAH5urT25Drv6/0puv89a+/8irFe3xh/i65Gif0/iJltpyLSJW0ez9LK/x0pu16JOZ/E+SrdXieIu5VPAudbayyOsl7T3UBKPrYiIyK9xcx/vO+9Rxphl1tqovWEapsrG0rWeM8VVPNfsMcZ0pmSumQaPlNwetNZe1IyBxBTrNVm4yrF4fvuYz2Kl91X7uq92vvkG4Nii21uAM621odnQ9WyCWYXHSm43+9iKVEUZ3SKtaWfRv3trvfFC88mXFN11dcQgN8yt4yf19WDRvztxmfZpcHTM5Y8puV38HqDQObz4MszS5UMZY45gdlOZqmoXGmOOKxnDf0UMckPC3kNJO7YiIiIA1tpx4Jcld9f7ysLiAG+cpLL9qthnTedMFSitxfw036WkEg+W3K7597o6WWSMiTNffQqzY1N+r8nS+WGs+Sazg9J+24vrVUX/ngbOKxfkLkjUPL4gacdWpCoKdIu0puJLNRcbY2o94VzG7Evproyx7kk1HouEK71s9+VNGUV8f1PF8nuBW3yW2Vb078WFAGtUpa/bbb5LRVc6gazXe6hRlxAn6diKiIh4Pl9y+x3GmO467q840BUnK/L4Kvb5vJgZ4aVzrGr/3/1Vye1Km/K1sormY9bau5h9IuFlpqQrX4LFmctHeU2W3veCeMOZNd+cBm6MuX6p4rn8bdbauysYR1Ik7diKVEWBbpHWdHXJ7bfXePulE/dIDSsKwa+XlF1Q/Owt+ve8GOvV+7VQL881xkTKJjDGPJvZ2UPbrLX7fBYtzerKxRjPm0tul36pi6vS99BJwFNj7Gdvye04r504knRsRUREPN8D7iy6vQT492o3aowJ+r+4OJOxJ8aJ39OrGM5BwMuiLGiMOZDZSQ+jMQJ0Qa4tuf2OKrfXiqqZj/2s6N+HU91rpZHeEGPZ0nmj31zwd8y+YuKsQp3ssowxz8LVzPbcbK3Nxxifn+K5fJzmjW+JuZ9KvwPGkbRjK1IVBbpFWtN3mF0b673GmFpe6vZYye3Sy5WC/B/UG6BSxd2sI1/yZq3dzuwJ8guMMXEnWM3yrxGXK22K+o2A5b5ZcvvCQhmeUIWO6mcW3fUw8JOIYwvyWMntsu+hQgbPx2Pup7QLer0ul0zSsRUREQHAWjsFfKDk7vcYY/6u0m0aYy4EvhTw8E0lt18ZYXv7A++qdDwFHym/CAAfYvZVmUFzpsistduYnRH6XGPMW6vdboupZj72HyW3P2GMWei7ZLK8Icr3z0LCymuL7rqfuSdPvFJE3y+66wBgRcSxfKzk9v9EXC/MY0X/PibKVRXGmJcCp8XcT0XfAeNI4LEVqYoC3SItqFAf7FNFdy0CrjDGxKrPbIw50RjT5/PQn4BdRbffVsgQCdvWP1H/uoit7I9F//6bKEHEIheV3P6yMeYVcXZujDncGNMfZ50aOM8Y8/qwBYwxbwfOKbrrcQImWNbam5k9cV6COxaB/xcWXtf/w+z/L79srd0dPvSy/lBy+wJjzIIy6/w78UvP/LHkdqSMr7gSdmxFRET+ylq7Cfhi0V0Z4OvGmA8aYzqibscYc6wx5nLg0wQnbpQG6P4l7P/3QtbkpcDBUccR4GRjzAfDFijM/d5XdNckwQH7uNaW3L7EGHNylBWNMRljTLV/f9JVPB+z1g4z+wrNZwPfilOCxzivNsYcEnWdGpgH/E/YOI0xTwb+Gyh+H/5nSDPTz5Xc/pgxJrREijHm3cxOqhgHvhK2TkTFc/mDgdCTZ4UrVf8HiFt6pprvgHEk6diKVEWBbml3ncaYoyr5afbAI/gE8POi273ADcaY1caYA4JWMsYsNcasMMb8HLgBmBPoLjSf+1HRXQcDPzXGPNNne4caY77IzBeMh+L/KQL8oujfPbgTF2cZY55e7rVZmCAXX6a7EPiJMeYLYZkWxpj9jDFvMMYMAXcBb6vJXxLNY7iJ4DeMMR8uzVwxxnQbYy4CNpSst7pMI5j3AHuKbr8R+JHfJcjGmJcA/ws8vejuPwP/FvWPCGKtvQf4bdFdTy+MY5nPOJ5qjPkusKZwV5z30O+A4sDxamPMvxpjTjLGHF3y2tkv5p9RKhHHVkRExMcq4Lqi2wb4v8DvjTF/F5SwYYzpMsa8whizAdf/40y/5TzW2tuY3R+lF/ihMWaJz7afC1wDvIa5V3rF4a27zhjzmULwsHg/84wxK4DLmZ3N/cnClX9Vs9Zexuzs8CxwpTHm4qDgaiGJ4r2445qWqw0rVe187O+Z3ZjyTOB6Y8ybjTHz/XZYOIHwbGPMAHA78ENcpm4jjAMTwPOBnxtjnuMzvufjvt88u+juP+Pel76stb/ABcY93bjvoOcbY4pf2xhjnmSMWcfcOv3/bK19JM4fE2Bjye3/MMb8fenJs8JnyNtw894jiP9duOLvgHEk7NiKVEUlBKTdLWF23b44Et0IxFo7aVw27E+BEwp3Pxm4GPh3Y8zNuOBlHpfxfQCuEU7UjIpB3GVmXpbKc4A/GGNuxJ15zuCaVvYxc1JtO+4/xM9W+Ge1sw24S2+94/0Sguud+702/z9gKTPZBhng3cC7jTE7gFtx5W66gP1wX8yOqsG4K7UWWImrRfhvwBpjzK9wpS0OBl6Im4AVu5wymUnW2lsLX/Y2MHOczgC2G2Oux02wu4BnMrdh5BPAm6y1Y5X9SXN8GPf+9MbxcuBPxpithXHMB45m5v0LrmbhtYV1y7LW5o0xlwL/VLhrIa78iV8JlEHmZv9HlrBjKyIi8lfW2j3GmDNwgZyzih46vnDfdGFu/AAuEPUk3BzkWFzQttQun/s8H8QF1b3/C08F7jTG/Bq4FzfvPh431wLXvO3tuHlMJf4TeDWuTu5K3Nzul4W/ZX9cY7n9S9b5De7//Vp6F27uv7xwex6wGpfVfhOwAxcA3Q/XWyVOz5FUq3Y+Zq291xjzOuAKZoLVx+JOLnzFGHMDruTHbtxr91Dc66GejVfDPITL7P0/wPNwQfmbcd8RDe75L23Auht4q7U27L0FLrHiBGYC5E/GzT0/YYz5Le7Ez2G47wqlV1N821r7RWrAWvtjY8wvmPk+thD4Ku579u9w37EPwgX79yssMw38A/CDGLuq9jtgHIk4tiLVUqBbpIVZa/9ijPlb3AS4+HKqDO4/sGf7rli0CebWlPO2fWvh7PT/MLsxxomFn1J34OoUvjTK2GU2a+1dxpXp+C/cRCru+tPAWwtfND6OC6J6lhV+ynm0/CI1sxPoB67CBbazhNe0+wGQs9aW7Wpvrf2qMWYXbjLqHUuDm4g/L2C1+4DXWGtviDb88qy1PzPGrMJdAu1NTDtwX0j9up3/Gpf19d6Yu/pn3JeJujeCTcqxFRERKVWoQ3u2MeaduCvdirO4o86Nx3EZp2FZp/9rjFmJS+zw/n/vAl7ss/gE8A5r7feNqThGtQd4Fa68xTG4OV5YaYxfA2dYa/eELBObtXbcGHMqLumg+CpAQ/D3g3ZS1XzMWvtr40pKfpfZc6oFuMBjOXuYfeVdvX0cl8H8zsLtZxZ+/OSBc6y1pc3N57DWPmFcrevvMft1fiAuwSLIJcwu3VMLbwC24AL3nsNw8/VSE8A7rbU/jPNer/Y7YBwJO7YiFVPpEpEWZ63dZa19K25C9F2gXMbkFG4CPAAcba39Vsi2v4vL2rguaBlgFPdl4nnW2kqz5wWw1g7hSj0M4iZVXuZGnG18EpdB81lcpk85fwT+H/BCa+274+yrWtbaG3Ffiv6L4MypHbgviGdaa/fF2Pa3cZlUX8FNroPcjzvex9UjEGut/SzuBFBp86pi24F/AV5irX24gn2M4SarZ+OaRt6KO4EVVP+wKkk5tiIiIn6stV/CXbX2Plxmc7mT5BPAL4ELgCOstYPlsk6ttZ/DBZ9vCVhkGtgMvMBa+/Xoow/c3w7cVZSfJTgxYSeuDNqLrbWPVbvPgHHstdaehwu8bgb2hi2OK5P4QVyd8pZWi/lY4bvU3+D601wXYd1x4Me4qzgPt9beFXvgFbLOu3DJVr8PWGwCVwLkGdban8bY9mO4KyHfHLJtcN9rfwa8yFr7nkLiT81Yax/EZWz/P4K/k+0DNgF/Y639WoX7qfo7YIx9PUYCjq1INUyE5DcRaSHGmE7cf8hH487OduMmQQ/jgpq3VlI+oFCH90XMdIO+H1eq4Nf6jy+5jDHH47KXDsJdVrcHd1nan4BbrLU7GzSOk5ndwOnviyeDxpgsLgPmSNzltztxXx5/EyWLu8y+u3Cv3afisscni7Z/Q7XbjzGOZ+AyuQ/GTfzvB+6w1m5rxP7rISnHVkREJEihpvVzcf9XHYTLvs4Dj+DmQ9dXk/1c+P/9+cAhuMDvPbj58X1VDj1of/Nxc6ZluP97HwZGgF9Ya6fqsc+QsXTj5gFH4o6twQV3/wTc2Kh5ZqsqzI//FleO80BmXrsPALcBf7TWTjRoLFuYuXJ3h7X2qJLHT8CVU1mCm+feC1xbSRKHz76XAifhSrZkce/dUeA6a21DrkgtPBcvxl1V0YMr33If8MtGjaEeknBsReJSoFtERJquXKBbRERERESSqVygW0SkUVS6RERERERERERERERSTYFuEREREREREREREUk1BbpFREREREREREREJNUU6BYRERERERERERGRVEtcoNsYs8AY81tjzE3GmFuMMYOF+y8yxtxnjLmx8NMfsP4rjTF/NMZsN8asaezoRURERERERERERKTREhfoBvYCp1hrTwBOBF5pjDmp8NhnrLUnFn42l65ojOkALgHOAI4H3mSMOb5B4xYRERERER9KZhERERGReuts9gBKWWstMFa42VX4sRFXfz6w3Vr7ZwBjzLeBM4Fbaz1OERERERGJzEtmGTPGdAHDxpgfFx77jLX2k0ErFiWzvAK4F/idMeYH1lrN8UVERETkrxIX6Ia/Tma3AccAl1hrf2OMOQN4jzHmbcBW4APW2kdLVl0C3FN0+17gBeX2d9BBB9mjjjqqJmMXEZH4nve855Xe9V99fX3/1YyxiEhr2bZt20PW2oObPY521+hkFs3vRUQap2Quv6yvry/q57uISEWC5viJDHRba6eAE40x+wGbjDHPBL4IfAw3If4Y8CngH0pWNX6b89uHMeadwDsBli5dytatW2szeBERERFJDGPMjmaPQZx6J7Nofi8iIiLSHoLm+Ems0f1X1trHgC3AK621D1prp6y108CXcZkdpe4Fjiy6fQQwGrDtL1lr+6y1fQcfrCQfEREREZF6KszlT8TN0Z9flMxyNK43z/24ZJZSkZJZNL8XERERaW+JC3QbYw4uZHJjjFkInArcbow5vGixs4CbfVb/HdBrjHmKMWYe8EbgB3UesoiIiIiIRFTPZBYRERERaV+JC3QDhwPXGmN+jwtc/9Ra+yNgnTHmD4X7XwZcCGCMWWyM2QxgrZ0E3gNcCdwGfMdae0sz/ggREREREXGUzCIiIiIi9Za4Gt3W2t8Dz/G5/60By48C/UW3NwOb6zZAERERERGJ63Dg0kKd7gwuIeVHxpj/NsaciCtFchfwLnDJLMAGa22/tXbSGOMls3QAX1Uyi4iIiIiUSlygW0REREREWouSWURERESk3pJYukREREREREREREREJDIFukVEREREREREREQk1RToFhEREREREREREZFUU6BbRERERERERERERFJNgW4RERERERERERERSTUFukVEREREREREREQk1RToFhEREREREREREZFUU6BbRERERERERERERFJNgW4RERERERERERERSTUFukVEREREREREREQk1RToFhEREREREREREZFUU6C72b7yFejoAGPc7698pdkjEhERERGRSk3k4bq3wDfNzM9tlzR7VCIiIiItr7PZA2hrBx4Ijzwyc3t6Gt7xDvjQh2DnzuaNS+orn4ehIRgZgd5eyOUgm232qERERESkWjuH4eoXz73/hvfAHz4Cb3i08WOSxpjIw44heOxm2PcozD8AnvwMWJaDLs31RUREGkGB7mb5u7+bHeQu9pe/wH//N7z1rY0dk9Tf8DD097uTGuPjMG8eXHABXHghfOQjCniLiIiIpNVEHq4+Ofjxycfgji/Dsf/YqBFJo+wchi39MD0B03tm7s8sgOtXwcmb4ZDlzRufiIhIm1DpkmYYHYVvfCN8mbe9rTFjkcbJ512QO593QW6AfftgYgLWrYPDD3eBcBERERFJn9vXA1Phy2x9Z0OGIg00kXdB7sn87CA3uNuThccnxpozPhERkTaiQHcz/KOyONrS0JDL5A4yPg6nnQZjmgSLiIiIpM4t66ItN5Gv7ziksXYMgQ2Z44PL9L57qDHjERERaWMKdDfDVVdFW+7UU132t6RfPg8bN85kcgfZvRsuvbQxYxIRERGR2pjIw3TEAPZ3D4DRiN8HJNkm8nD3RpgqM8ef3gOP39qYMYmIiLQxBbqbYXIy2nI/+xksWQJf+EJ9xyP1NTzsnsdrr422/Pe/X9/xiIiIiEht7YiTrTsJW06H+xXsTrWdw7BpCTwYcY6/9+H6jkdEREQU6G44Y+Kvs2IFPPBA7cci9Vdcl3vfvmjrXHONanWLiIiIpMlvKyhNeO0ZqtucVsV1uW3EOf68A+s7JhEREVGgu6EqCXJ71qyp3TikccrV5fYzNeWC42mo1Z3Pw4YNsHq1+51XzUkRERFpM9+sdI4/Ddu/VNOhSINEqctdqqOrPmOp1kQetm+AG1a73xN5//tERERSoLPZA2gbV15Z3fo33liTYUiDjYyUr8vtZ3raBcnPP9/dzufd7ZER6O2FXA6y2dqONa7hYReQn552f2N3N6xaBZs3w/LlzR2biIiISCPsqrKfzp++Ak9fVZuxSOPkR8rX5S51xyXwjI9AV4+7PZF3AfP8CGR7YVkOuho8v9857DLT7bT7ezq6Ydv7AON+vPuuXwUnb4ZDNMcXEZFkU6C7Uc46q7r17767NuOQxurtdQHguMHu8XHYvt0FuD/+cfjsZ90VAXv3JiOgXFySpXjM4O4fHYWenuaMLY2SeCJDREREyrvuTdWtv/v+2oxDGivb6wLAcYLdk2Nw40fgbz7r6rP//EywU2AnoGNR44PJxeVXPH5/j3ffln44a3QmUC/lJeFkhohImzHW2maPoen6+vrs1q1b67uTasqWAHR2wsREbcYijZPPu0aUfiU95s8Ha4Nrd3uB4qASJtlsYwPKxcHYBx6AjRth1665y3V2wpvfDJ//vIK1UfhlxmcyyowXEakRY8w2a21fs8chjdWQ+T1UUbakwCyAN+2uzVikcSbyrhHlZAUlPbqPhvE/+T/W0QMnroVd99Q/MLp9A2xbGT1Y39ENfevh6PPrM55W45ctbzLKjBcRqZGgOb5qdKdF3DrPkgzZrAtYZrMugAnudzYLP/iBC3QHGRsLr9PtlTdphOFhF7BfuRLWrYNvftM/yA0wOQnf+pZbXk01wxVnxnsZ8ePjM/enoU67iIiIVEFJR6nUlXUBy86sC2CC+52ZD5kF4esGBbkBpsbghg/CbetcEHrTEhcwrYe45VemxuHuy1SvO4ribHnvGE+Nu9tb+tWEVkSkjlS6pBHuuKP6bXQltHmJzOVXhmJ01N23fTscc4y7r6enuhMY4+Pw5S+7YHk9S134lSmZnAxfZ2LC/aiMSbiwZqWlddpFREQkWR6vwRx//gHVb0Oa45DlrpTH3UOQ3w7ZY+DwfvjRcTC9p/LtTu91v+tdMqSS8isPXuOC78pKDhfWrNROu9eMMuNFROpCge5GePvbq9/GokXVb0PqL6xBo1/Acmqquv395jdw8821r9ldWqak0nEqWBsurFmpV6ddREREkumXb65+G8e9t/ptSGME1VsuDVievBmufnHt9lvLwGjx37DoyPjlNaf3uh/V6w4Xli0/Ne5OjIiISF0o0N0Id95Z/TYOO6z6bUh9VdKgsbOzfHZ0ObVuAlkarC83RmOCS7AoWBsurFlpd7fL/hcREZFkevyW6rdxrALdqeBXbzmoeWStM51rFRj1+xuYdo0wMbPvw8D0JNiAXkLKSg4Xli3f0e2y/0VEpC5Uo7sRnvKU6rfxjndUvw2pryhlKEpdemnt9l+Lmt1+NaPDgtyLFsFJJwWX1pk3D268ETZs8G/I2e5yOdd40k8m4x4XERGRZDId1a1/6KnKiE2DSuotL1xau/3XIjAa9DdM7QYy8JxPwPFrXLPJs3fC2Q/CYacEb09ZyeGW5VzjST8mA0s1xxcRqRcFuhvha1+rbv358+Gd76zJUKSOKilD8eY3w/77R9t+Z2f45YW1yJ4OC9b76eiAyy6DBQFNd/btg5/8xDWxVHPKucKalW7erNrmIiIiSXbYqdWt/7f/XZtxSH1FqbdcKlZJmgyYedCx0P/hWgRGw/4GLHQsgBPXugztrh73c+Q5M40254xpHjx6I2zfoOaUfoKalXYW7tcJLhGRulHpkkY49lhYuBB27463Xibj1vvJTxTwSoNKy1Ds2AEHHwx79wZvu6sLPvMZVyJkzZrgfRxxhMueLm6EGbVJZT4PGzcGB+u9cUxMuH1lMi4Ye/jh7ndxuZNStS6v0kqWLw9uVurX2LReTUdFREQknr/5Atz3/crW7bsEFqo0YSpUUm+5913w+4/MNJYM1OGyqJ9yngscl5YWMRkXGMW6oHJpffAoJvJw98b4f8OynCvP4sfug/t/AjuvCy7hklZBtdjj8mtWujTngty12oeIiMxhbFBt3TbS19dnt27dWt+dXH89PO950Zfv6IBPfALe9S4FBdMin3dZy34lOrLZ8ADv8DCcdpr/yZBFi+DKK11ANGwfCxe61421M40wvWB0uSaVXl3uvXtdFrafRYvg9a93ge3iYKxnbMwFZS+7DK65xj9wP28efPrTsGJF+HjEv7Fp1OdTRET+yhizzVrb1+xxSGM1ZH4PcPUrYOfV0ZfPPh1e+gN4kmr0psb2DbBtZXC95b71/rWqdw7Dta8MDjADvOxKOPy0mdsTY3MDo2EB8HLBZa8u99Te4Hrb5f6G4n0HycyH194Jiw4PH0/S+dUxj3qsk7QPEZE2EDTHV6CbBk6Ezz4bNm2KtuyCBS57VkGtdBkehjPOcFnPe/e6sjNdXfDjH5d/HsfGXM3u738f7r/fBZTPPBPOO292QNkvAGqMu71r19ztlguyhwXP42zHs3o1rFsX/HhHh3tdn3Za8DLtrpqTJiIiMosC3e2pYfP7iTx898lAxO9UCmqlz0QeNi2GSZ9a3J1Zl7UbVIpiYgxu+Tjc/hn3ErH7gE7IdMBLfgCLy8yHJ/KwaYmrrR173yHrxv0b7h6Cuy+DB68JzlI3nfDSK8r/TUlVzbFO0j5ERNpE0BxfNbob6XvfcxmtUezZM9MYcMxnUiXJ5p1AinMiqafHZTpfdRX84Q/u94oVcwOaXqmL9etdGZP162Ht2uD63eWaVJaryz1/frya0V4JlyBTU3D66XD55eW31a4qaWwqIiIijdeVhZf9JPry5ZoYSvI8ehPYqbn3dywqX2+5qwdOvBjOeRie/wXX8PEF/wHnPBQtIFxJffAo64LLwo5SM7qrx2V77/es8FIsdhK2nA73XB68TJJVc6yTtA8RkTanQHfSKaiVHsUnJrzyH/v2udu1PmHR0wPnn+8C3OefD/fcE78RpiesiSbAKae4wHrUKwtyOVdio5yzznLB/LTK51099NWr3e9yGfFxlGtsetlltd2fiIiIVG787vjrKKiVDhOFkxJTfr2WMrD/idG24wWLixs+RlFJffAo6wIceorLII56ZUG2N7g5ZbHrzoL7UzjHr+ZYJ2kfIiJtToHuRjssZtOZ4iBlPQNrUr1mZuGGZVF3drpAddDrJWzd7m4455x4ZTK87O+urvLLnnlmOq9YGB52pUVWrnRlWlaudLeHh2uz/XJZ8ddcU9v9iYiISOXyI/HXKQ5qTeRdHegbVrvfE5rjJ0ZoVrSt/8mK0OByJ+weDX69hK3b0Q1Lz4lXJmNZzpXdieLnZ6bvioVyxytbg7r61TyfIiISiQLdjfa2t8VbvrvbNf6rd2BNqlcuCzcsq7paYVnUk5MuAzjo9RK2bibjHo9r+XLXdLJcZvfkZPquWPAy9/P5med7fLy2pYbKZcXv3ev2d8YZ6TxRICIi0kqyvWAilif0eIGzncOuZu+2lXDbOvd70xJ3vzRfszNwQ4PLk65udtDrJWxdk3GNLuPoKpQ5ycwvv+z0RHOuWKjmpFGtj1fcfTAJd34TLjsYRlOYES8ikhAKdDfavoBu10EyGRc8q3dgTapXLjP6mBpkAQTxsqizWVi0aO7j3uvljDPg85+ffVVA8bre+Lu749Xl9nPeeeWzuicn4dprK9t+szQic7/4OQmr6z82Bh//ePX7ExERkcoty0FAq5RAJgOH97uyGJP5mWCqangnSyOyfMN4weXOLGR85vhhr5fidb2/oaM7Wl3uIIcsh9fe6RpPhpqCx2+Nv/1qVHvSqB7HK2wffs8nk64O+pbT01n+RUQkARTobrRyJQm8oFZnp2sCuHEjXHGFGtOlQT0yo+PwmlSee25wgHlsDD74QXdVwIoVcOCBLuh9wglzG1zGqcvtJ5uFH/yg/HKXXZaukzWNytz3ns9TTglf7jOfSdfxExERaTVdWThuZfgyXhZsceBs9IrojelU3qQ5GpHlW84hy10t7WXnggmY43uvl9LXyf4nuHX71rtGmH3r49Xl9rPocHjpFeWX2/tw5fuIy6ulXu1JI+9Y1/J4Be1j2blAyAmDNJZ/ERFJAAW6Gy2XA2v9H5s3D4xxQdHJSVee4IwzXCCwWSUxJLp6ZUbH0dPj6sBPTAQvs7fQLX3fPrfcunWweDHceOPsBpe1GO9pp8GmTeHLTE/DpZdWv69GaWTmfk+Pq5EeltUNOtklIiLSbM/8CGQW+j+WWQDP/P/gyc+EBYfAklfD/EPg7o3RymKovEnzNCLLN9I4emDBYWAD5vhT43Dbp+GyQ2Db+2e/Th69sbJGmGEWnwZHnBW+zK4G1psOq6Uet/FrpY1D4/CeTyaDl7FTalgrIlIBBbqbYWrK//59+1wQsjh7e3oafvKT4G3VuySGxONl4dYyMzquclcN+Bkbq18ZnNe9Dt7whuDH9+2DVavSU2++0Zn7YSfHwB2/tJV/ERERaUlBV2Dugd9/BB6/GcbvhB3fgiuOgweuDt6UVxajVpmqUrlGZPlGEdrIEHjiVvdam9rlbtf7dbK4353ECfKXXzTuhEyza6lXIttLaEa3nUjmuEVEEk6B7ka79NKZjNpaaERJDImnp6f2mdFxlGtkGKSeZXBe8Yrw4Pu+fbUNtOfzrgZ5cS3yWml05n42CxdeGL5M2sq/iIiItJo/X+pq68YSkPwCM2UxapmpKpVrRJZvOaGNDEPU63WyLAeZkH4803trH2gPKuHT7FrqlViWg0xHmYVCkl1ERMSXAt2N9qMf1W5bCxbMDazVM8An6eAXiC1X+gLqWwYnSvB9asrVDa/2tTs8DEuWwMqVrizLypXutpcxXov3SKMz9z/yEVezP0hHh8qXiIiINNN9NZrjZ+bPLouRxkxVqQ+/MipR1Ot1UjweE/JdY2ovDL+++tryYSV8klBLPa6uLLykTD+jOy7RVRsiIjGVa5csSXbBBbMDa8PDLit2etoFLbu7XUmIzZsbWzpDms8LxA4NueD1EUe4gGxY1m89y+B4wfdTTgmuH75rF3zrW+7xSl+7+bx7DxQHr7369v39rrnruefOfo9ceKF7L4Er+5LLufGW42XuN0I262p1f/Ob/o+rVr+IiEhrOPQUWP6dmYxhL1PVL9id1ExVqR+vjMrdQ7D9y/Dwb8qvU8/XiTee4dfD/QHlNu0+99jO6+D6VS44HrfsS3EJH4/3ntjS78Zw8mb3bzvtHjPzAAu97yaxmdGLT3ONbP/4Wf/HrXXP9dEN+s4hItIClNHdaK9+dW22090Nxx8/c7s4wOcF9sbHZ+5XWYP2U1xCZcUK+PGPw8tq1LsMzvLl8OlPh2cme0HwSl+7Q0Oza9wXm5qC17527ntkbMxlfvtlfxdr9tUSL3tZ45pgioiISDxLajDH7+iGpefMLouRxkxVqS+vjMrR74iW2V3v10lXDxx5TvmxVFMzPEoJHy/oftx7IDMPjHF1ru+4JLxWeFA5lEbJhGXD66oNEZG4FOhutPPOq812SoOS5QJ8tSgJIem2fDncf797HcybNxNwrmd96VLnnRetjIonbt3wkZGZIHapXbuCG8F6ggLs5cqhNEKjm2CKiIhIdE+twRzfLyDpV66io9vdXr4Rdny7eQE6aa5yNbu914lXBqeZYylWSc3wyCV8LNzxBZjeN1MzPyzAHlYOpVHSWF9cRCTBVLqk0bJZOOggeOihytbv7nZBrdKgZLkAX7UlIaQ19PTAxRe7ms9eWZNjjnFB0kY0zfQC6sUldjo7YXLSf/nxcddo8Y47opUV6e11r3G/90JXV3DZlFJegP3888uXQxkdbd6xC/o8EBERkcbqykLHk2DqifjrmnnQMT84IFlcriK/3QW+Fh0J1507U6aho7vyshCSTt5JkOJyHZlFwBQceS4c9jJ34qQRjTP9xhKkXJbyRN5lcOdHXBB4WS56CZ8omd9eGZAo5VAaceyW5dx714+u2hARiU2B7mY44oh4ge6uLhecNiY4KBkW4IPZJSGgsQE6SZ5G1pcuVVo/fHTUBbODXrvXXOPKrngnaTZuhLvvhptvhkcfhQMOgGc8w70vcjm3jJ+ODvde2rWr/BiLa16HXS1RHBBvhNJj18iTFCIiIhJu/n6wK26g28DzPg1POS88qOaVqwAXoNu0pPkBunbgF3TtitDLpVH8ToI0KrgdNpa7L4MHr5nJqi7W0Q2LjnBXIZQe1/uvgp+fCXbKlR3pWOSCwC/eGK2ET5zmrXGC4vXkd5Kgo9v9XY3IxhcRaTEKdDfDs58NN94YbdmDD4aPftSVfAjLZA0L8PlpdIBOpFhxoD2fh02bgpfdW5gge4Hw00+HBQtgz56ZZRYsmLlSISjr2WtEGUVxzeuwqyWa0QTS7yTF6Ch86ENw++3wtKe5uuyLFzd2XCIiIu3u4BfBjrtjrDAPTv6ha0gXR1ICdK1u5/Dc4GMSs+aLT4I0mzeWpW9wJ2P8At1Mw41rXKPF4uP6rIvghg/MXnSqkKBy3bmuVM/wuf7BYKwLnD96E2TmBwfYi8uAxAmK11vQCYuJJ+CX50H+dsg+DU5cC4s0xxcRCaMa3c2wdm30Zf/yF3j/++HAA11t5aD62l5Zg2x2pmFdZ8h5jGYE6FpRsxsUtgK/1265Ot7FQW7vdj4PZ5wBJ57oAr/r18OaNe736CicdpoLdkdRXPPau1rCTxKaQL7zna5e+Ne/Dr/9rfu9ZIlr/CkiIiKN85xPxlxhEn7xaldjO0597SQF6FpVcVkL71hX00yx3QTVlu/oAQxMjs09rqVB7mJ2Gnbf44LBfevh+DXu91mj7nGvzvYDVwYE15lbBiRptbG9kwQnrnW/f/9RuHwJ3PV1ePi37vflS1wNchERCaRAdzMsXgyXXBJ9+elpV3pk3Tq3blADPK+sgRfge/Obkx2gS7skNChsFaWv3VNOqWw7Y2Pw8Y/PZD2vXet+e6U97r47+D0BrkFnaWPOJDeBfOpT4ctf9n/sAx+Ab3+7seMRERFpZ4sWQ1+MOT7TMD3hmuB9b3H0BnhJC9C1oihZ8xLOy1J+zifg8FfCIS+GI15T2ba8EzilwWDs3BMSpYKacoY10Gx2beyf/C388TP+j21dAbsfaOx4RERSRIHuZrngArj8clc3OI6xMVeWYSwgi6A4wPf5zyc3QJd2xQ0KvbIW4+Mz9wc9PxKs+LV7zjnhAekwn/wk3H+//2NhZUjABdhHR2c3avXLOO/unhsQb7RNm+DOO8OXedOb4KqrGjMeaX26gkVEpLxjL4DllwMx5/hTY9EzhZMcoGsVypqvjUdvhBs/BDuvg/t/Aju+O1OOJK7do3OvfAg7IWHmweFnzGR+l5abCco69wuKN9KD18Ejvwpf5ncXNGYsIiIppEB3s+Tz8Na3wtRU/HW9+trlJDVA1wqiNCiUyoVlUZczNeUynf0y68uVITnnHP/3RWnGuVcOZfnyucs2ypveFG25175WJ16kerqCRUQkmok8/PqtQAVz/KiZwkkN0LUSZc1Xz6/8C5OVb+/uy1yJkuIrH8JOSNh9sP8JLvM76D3hZZ2XlkNpZg32X7yu/DL3bY5X7kgkzETe1bi/YbX7rdeWpJyaUTZLWKC0nDj1tb0A3dCQW+eYY1wQUUHu6iStQWHa5fPuNToy4oLRudxMU8ndu2Ey5qR4zx637ujo7Nd6WNPWclc5+DWBbKa9AfUH/ZYrbmR70klw2WVqVinRFV/B4vE+//zeZyIi7Swsw7ScOJnCQc3rFOSujWU51yDRj7Lmo6nmveDHC2hv6Xev/a6emRMSfsHuqCckktTME2DikfLL2L3w3ScV3dEBL/ouLDurbsOSFpWWprsiMSQuo9sYs8AY81tjzE3GmFuMMYOF+/+vMeZ2Y8zvjTGbjDH7Bax/lzHmD8aYG40xWxs6+DjKlVAIE7e+dlC9Yqlc0hsUpklQpii4ANqb3wxdXfG362XWF5dbGBpyDSlb4SqH+fMrW+/Xv3bH9wtqZCMR6QoWEamBtpnjh2WYlhM3U7i0XrGC3LWjrPnqlX0vxCzv4ym+8qEly/hUclym4H/Phu8fo+xciU5Nd6VFJTGjey9wirV2zBjTBQwbY34M/BT4kLV20hjzCeBDwOqAbbzMWvtQg8ZbGS9QWkmwW/W1m6+azGCZESVT9POfd/WoJybibXt8HK69Fi680AXjxsfdey6TccHue+5J91UO3/oWnH125euvWOHWP+yw2o1JWpOuYBGR2miPOX5Yhmk5qQ3MtShlzVcn7L2QWQRHvg7u2QjT++Jt17vyYSLvssaXvAbu+Z57/0ztcvs0mfSekDjsVHjgysrWHf8TXHYwmE5l50p5UZruJulqB5GIEhfottZawDt11FX4sdba4o5qvwbObfTYaiosUBqksxMWLkxf5mkr8jKA+/vnBlH1/EQXJVP0/PP9j/X0tKvHvS9gcrxokQtoF5f48IJ1556b/nILZ53lTpiNjFS+jfe9D047bXbJmOIyJyIQfmJWV7CISERtM8cPK3kRxHRBx4L0BuZaWdLKWqRJ2Hsh0wHP/0/offfssgmZRcAU7HciPHYjTPuU6uvoBqyr1128np2CZW+Bw16W7hMSJ30VLl9S+frTe3HnFfEv9yLiUdNdaVGJK10CYIzpMMbcCOwEfmqt/U3JIv8A/DhgdQtcZYzZZox5Z8g+3mmM2WqM2fqXv/ylJuOOxa9RZEfIZUodHfCWtzS/AZ7MSGKDwrSJminqd6x37oS77oIFC/zX3707uI51q5RbuOMO+NGPYN68ytbfuHGmZMyKFXDgga7ES16XOEqRsOawuoJFRGKo9xy/6fN78C95gQlf57BTm98AT6TWopR/KW0G+Tefg3Megpf/FDIB89upvXD7Z2eXW5je5QK89/0g3UFugEWLoe+S2m5zcjdsXaEyJjKbmu5KizIuuSKZCjX6NgHvtdbeXLjvX4E+4GzrM3hjzGJr7agx5hDcpZDvtdb+Imw/fX19duvWJpX6GxubaRR5xBEuiDcWUAvpyitd9qVIq9iwwQVagzJF168v3wByeHh2tndUa9a4uvWtIp93DSaDPj/i6OmBH/9YJ21kRun7rPgKFr1OJOGMMdustX3NHofMaMQcv6nze3C1Tb2SF7tHYcdGF4wr1dHtgnzKGpZWVfxeiFP+pbRJXhSt9H7a/QDcuAYevxUevQHsZHXbK75yRCfVBNyJj01L3EmjUp1ZXQUgiRc0x090oBvAGDMAjFtrP2mMOQ/4J+Dl1lqfmeKcdS8Cxqy1nwxbrukT4WJXXQWnn+7/WDab/nIL0l7yeXciJ6g0Rj7vGiP6ZRDHeb2PjcGll7pyQEGlTIrNnw+XXFI+iJ42w8Nw6qnBmexxxDn+5Z5naQ3FJ2bTWtte2pIC3clU7zl+oub3CiZIq/HqY+dHXFbospzL4K75fsbgzktdCZSotbyPX+MatLaSncNw9UtwF7ZUqfQzJ+y5bNTzLM1TekKpuMa9TohIwgXN8RNXo9sYczAwYa19zBizEDgV+IQx5pW4xjQvDZoAG2O6gYy1Nl/492nA/2nU2Gvi7rtdbeFdPn9icc1ikaTzywBdtWp2Bmitap339LjgdVdXtEC3ta1ZbmH5crjzTpfZXa2gz5vSoPbSpa7medjzLK2hp0f//4hIxdp6ju+VcCgOJphO9++D/hYmnlCgW9LDLzBWr4aHXT2Qme+ykYkwx8/Mb81yC4csh2d9DP7wkeq3VdxkMOy5hMY9z9I8arorLShxgW7gcOBSY0wHrob4d6y1PzLGbAfmAz81xgD82lr7T8aYxcAGa20/cCiwqfB4J/BNa+1PmvJXVGpkxD/IDbNrFoskWT7vgtfFmdpeWZH+/tmZwl797WozRcPqfZd6y1taNxP18MPhuuvgxS8OX27BAtizJ/hxv8+b0pMXfiflvOfg5S+HT30KzjtP2d0iIgLtPsf3ggm//SfY8Y2ZMgQPXOkaz/VdAsde0NwxipQzkXfBz+KrE+rd8DCsYd4c0y5I14qe9j64+f+ALRPw71gEU/uAgFInU+Nw92Xw2M0w8sXZDT//+lye4ZLHp8bmPnbNy+E5n4Knnqfs7lahprvSYhIX6LbW/h54js/9vqdmrbWjQH/h338GTqjrAOutt9dlRAYF7O65p7HjkdlUoiGaoSEXDPXjlylci0zRcu+dYps3V7evpFu+3L1W3/9++OpXZz/2vve5+uTHHRce6F64EH7+c3jBC+BpT4N//de5Jy+CTsqBy6z/wAfgwx9WdreIiGiODy5ze8c3/B/bugKOPBsWHtbYMYnKM8SxY8hl+PopzhSuJa9hXpRg937Pa91M1K4svPxncO1pMLV77uOLXwsvKny+bF0Bd30L7IT/th74Kdwf1PcXmJ5wV8D6PrYPrv8A3PRhZXeLSCIlvkZ3IySqhl+UhnL33w+HaRLccGrGFt3q1bBuXfDj9WgEGVbv20+7f/YND8MZZ8RrXjlvXrTSMKXUX0BEmkg1uttToub3nl+eB3d9Pfjxp5wHL/xaw4YjqD5tXDeshttC5vj1qI8dVuO+lJkPbwpJ5GgFXt3y+65wt5e8yn12FAf44xyzaqjHgIg0UdAcP9OMwUiIbBZOOil8mQ98oDFj8eTzsGGDC15u2BA9kNhKiktxeBnD4+Mz98cJFrYDL7vaT3e3K09Sa1697/nza7/tVrR8uTtptnq1C2B7x23hwuB1Kglyw0wWv4iISDt7/Pfhjz/2h8aMQ5ziMhxetvDUuLu9pd8FFGU2L7vaT0d3fepjezXuo7A1aMiedF09cOwKeNlm93PsirmBZu+YdWaLnq8YF/Nn5oOZV345L4tfRCRBFOhOonJ1uH/968aMA1zW55IlsHKly9BdudLdHh5u3BiSIEopDpmRy7lsdz+ZTP0aQXrNGMtxNT6lpwcuvhgefhguucRl2j/rWbXfj/oLiIiIwL4nwh+feLz+Y5jIw/YNLjN3+wZ3u11FKcMhsy3LuYx3PyZTv/rYhyyHxa+OsKDm+H/l9QXoWw/HXQhMRV830wWZCIHuqXHXwFBEJEESV6NbCM6E9Sxa1JhxxGko2OrCGh0qiDeXl10dVOqlnq+bww+HQw+FBx8MXuYpT6nf/tOouEb6smWVbSOsrImXxa8a9yIi0s66nlTm8SfXd/9+ZTquX9W+ZTrCmhwqgOfPyxQOKvdSzxIWz/9P17g1zCLN8Wfxmgxu3xBt+eLnEtzzPL1vdsPK0uUXHeG2rxr3IpIQCnQn0dvfDv/8z8GP//3fN2YccRsKtrKwRof1KsWRdsuXu5MhQ0PuRMAxx7jAZiNOjnzwg+HvoRUr6j+GtIpSnsR7L3gnLzZudMHrCy+ECZ+mN5kMHHmkuxqk+MTHqlWtW+NeQX0RESm137PhsRtDHq/DVVWe4jIdHi/Iu6W/PevshjU5rFcZjlbgZQrfPeROBmSPcZnc9X79LFoMS14H910evMxxmuP7yo8AZfoTdR0Iz/3E7OfyrFFXD3zbhQGNLS3csMb9bpeTZ2peK5J4akZJApvV5PNw0EH+Aad581yZgUYEC5vRUDCpwhodqtFe8uTzcPDBsNcn+2D+fHjoIT1fQY47Du64I/jx3l732eB38iKoYevGjXDuue3z/lHjWpFEUTPK9pS4+T3ArtHwjNSz7oeFdWo4v30DbFsZHNTtW+8yP9tJWMM+NdlLpok8XHaQyzIulZkP5zyk58zP9g3w23cSGuw+9BXw8qv8H/Nt2mrATsHU7rnLZ+bBcz4FTz2vtYLAal4rkihqRpkm2Sz87GcuONLR4e7r6HC3f/azxgWEmtFQMKm8UhzZ7Mwx6e6eub+VgnStIJuFq692z1Fn4cKVzk53++qr9XyFWbo0/PGjjnJXcqxd634XH0svi3/9encibP16d/vuu4OvDtmzx2XYt0qTWzWuFRGRIIsWQ98l/o9l5tW3VIbKdMzl17Cvo9vdrncZDqlMVxZO+VkhwFiY45tOd/uUq/WcBVmWA9MVvsyRZwY/Vlzv+/g17vcJawkMJ03vg+s/4E4k7WyR3l5qXiuSGipdklTLl8MDD7hL32+91WWgHnAA3H47nHBCYy6Bz+VcaQE/9WwomAR+ZQeaWYpD4it+D+n5iu7MM93JgLDHwxTX+/aE1bifmIBvfhM2bWqNjGeVfBIRkTBPeStc/y8wXfL/4vS++pYQUZkOf80qwyGVO2Q5nP2AnrM4urLw0h/CltP9H+9YCE85r8w2emZf9XHD6uCTZwB2H0zW+XOtkaI0r223q2JEEkqB7iTr6XFlBC68sDl1bZvZULCZ/MoOFB9zBanSwy/oKuHOO8+VJtm1a+5j8+bB2WfH32ZYjXuAycmZjOe0lzFR41oREQmzYwhMwGP1DJYsy7nauX5MxgUK24VfjV0FqNKlNOgq5S0+DV68Ca47F5jGlTHpADJw7HspW8O7VNjJs2JpDgIXf1Y8epOuihFJCZUuSbIkXAIfVIog7VmXQZJwzEWa7T3vga6umdJJnkzGnXwbjnkJYi7n1i3Hy3hOM5V8EhGRMM0qIaIyHc7OYVdOYdtKuG2d+91K5RVEguwchl+9zdUy/2tQewqYgDsuif8+WJZzJ8nKSWsQuPSz4sFrg5dthatiJvKulvsNq93viRYpKyltSRndSZaUS+DbKSs2KcdcpBmKr2aY8OmsvmeP+4mbeV18dciePf7bhpmMZ7/SQY0o11QL7VzySUREymtmCZF2L9NRXGPX4z0PrVJeQcSP32u/WCXvA+/k2ZZ+V3ppeq//cmkMAvsdL+vTANWT9qti/JpsXr9KTTYltZTRnWS6BL7xdMylXfldzRCkksxr7+qQN71ppkFoqe5usBaWLIGVK2HdOvd7yZL4WeTN0g6Na/N52LDBlbjZsKF1GomKiDRCWBZkI4IlXsmHE9e63+0U2I1SY1ekFYW99ovFfR94J8+e+6ngZpfFn2tpyRoud7wy893vVrgqRk02pQUpozvJwura6hL4+tAxl3YVdjVDqUpP+vT0wOc/7xpP+gVHjYFLLpldIsh7Lya5frdfBnqrNq4t18NARETCFWdBFmfPmUy6gyVp0KyyMSLNFvbaL1bJ+6CrB45dAfudEP65lqas4XLH69BTYP8TWuOqGDXZlBakQHeS6RL4xtMxl3YVdjVDqWpO+oQ1uX33u12g209SSweFBX6TNtZqFWf9e9JwIkJEJGnavYRIszSzbIxIM0VtHFnN+yDscy1tZYPKfVYsPad1gr86ASgtSKVLkqwdLoFPGh1zaVdhTRRLVXvSJ6jJLaSrdFC7Na+N0sNARESiaecSIs3S7LIxIs0StXFkte+DoM+1tJUNaqfPCi+o70cnACWllNGddF5AqBUvgU8qHXNpR2FXM3i8zOtanPTxa3KbttJB7da8Vj0MREQkzVQ2RtqV32u/WL3fB2nLGm6nz4plOVdCxk+rBfWlbSjQnQZ+ASGpLx1zaTdBJUWMgRUr3O96n/RJW+mgdgv8pu1EhIiISCmVjZF2VfraX3SEu3/XvfV/H6SxbFC7fFa0U1Bf2oYC3SIi1fJrRpjNNntU8TX7aoaw+t3FWeRJOd7tFvhN24kIERERP155BZFyJvKu7EZ+xAVrl+VcYDCtmvXaj5o1HPd41/v5aZfPinYJ6kvbMNbaZo+h6fr6+uzWrVubPQwRSSO/ZoReYHZ5wjqIp8XYWHCwvZLjXa/AeD4PS5bMbs7oyWZbszmjXu+SQsaYbdbavmaPQxpL83sRqcrO4eAs10M054mt3PGMe7z1/Ii0vaA5vgLdaCIsIhVqx0BnM1VyvOMEZisJiLdj4DfsRIRIAinQ3Z40vxeRik3kYdMSmPSZc3ZmXfarsl3jmxjzzxqOe7wrWb6VMvNFBAie46t0iYhIpdqtGWGzxT3e+bwLQhcHxr0yI/39swPjfgHrVavKB6ybXe6lGdTDQERERFrZjiGXKezHTrtgbTuUtKi1oFIgcY93nOX9Mr+vX6XMb5EWpkC3iEil2q0ZYbPFPd5RA+NxAuJ+FPgVERERaR35Ef/GieDuz7fAHD9JWc5xj3fU5SfyLshdnPntrbelX5n5Ii1KgW4RkUq1ejPCpDR99MQ93lED48rMjy9prw0RERGRWsn2usxfv2BqR7cru5FmSctyjnu8oy6vzPx4knTyQ6QKmWYPQKSh8nnYsAFWr3a//Wr9ikSVy7l6zH4yGfd4Wg0Pu3rYK1fCunXu95Il7v5miXu8vcC4n+LAuDLz40nia0NERNrbRB62b4AbVrvfE5rjSxWW5VxjQz8m42pLp1VxlrMXKJ4ad7e39Ls62o0W93hHXb4dMvNrZeewq3u+bSXcts793rTE3S+SMsroTpu0Z9E1avx++7nppspq8IoEyWbd6yeoGWFa6zRXW8qjXuIe71zOvcf9FAfGm52Zn6bP9aS+NkRE0i7tmXTNHH/SslMl/bqy7vVT+royGXd/mstdJDHLOe7xjrp8MzPz0/SZrhIv0mKMtbbZY2i61HRl92uW5gV40hCobdT4/fZjjLu9a9fc5bNZBWekOmNjrdWMcMMGl6UbFPhdv765pTziHO8onzv5vMtI9rvCo96fD2n7XE/6a0PER1BHdmltqZnfg3+g1gvYpCFQ28zxT+Rd1uGkz//hnVkFaKQ6E2Mu8Jvf7oKiS3Ppfz3dsNpl7AY5fg2cuLZx44GZoPDjt8C+R2DeQbDf8eWPd7nnp1mfD/dfBT8/E+wU2AnoWASmI7mf6ds3uAzuoBMCfetV4kUSKWiOr4zutEh7Fl2jxh+2nyCqwSvVarVmhEkv5RHneC9f7j5fwgLjzcrMT+PnetJfGyIiaZP2TLpmjL84U3L3Ay6Y5Ec1eKVaXT2t9/pJWv3xsBNl5T47yj0/5TK/sS7IW8us69GrYMvps++bKiTbJfUzXSVepMUo0J0WaW+WVu34o17aH7afIArOiMwWVsoDIG1XAkUJjJcGxI84wv2dP/wh3HijW+aee2pbWiSNn+u9vbBgAezZM/exBQvS34BVRKTRklhGII5qxx/38v7SoBidwKT/sgrQiMy1LAfb3h/woG1s/fFGnCg7ZLnbzt1D8NitsO8hmHcA3PdD2HKGm+/XquTRRB5+8drgx73PxKVvSFZZk6Sd/BCpkgLdaZH2LLpqxu93aX9Qbe2w/QRpRA1ekTTJ5eDCC4Mfv+QS+MhHKs82jluTuhE1rIv3AbBmjZv4ln6e1LK2fxo/1/v74R//0f+xPXvgVa9q7HhERNIu7Zl01Yw/bm1tv6BYUJAbFKARCRSUtFJlMkvcE1eNOtHX1QPZ42DbhUUnyUrUIsC+YwimA64w8fbxwLWzx5GEngLLcm4MftLefFXakgLdadHsZmnVqnT8cS/tL5eJ6qe4KZ2IuCDyBRfAuoD6fdZWnm0c58RVJcuXihIkL91HmFqWFknj5/rmzeEZ3VdckbwsdBGRJEt7Jl2l468kkzMsKOZHARqRuXYMAZmABzOVB5craQpb6YmyuAF135NkAaoJsOdHCD35Zrrgno0wvXfmviSUqmrl5qvSloI+4SRpcjkXkPWThkBtpeOPcml/1P0sXOiCUt3d7nZ390xt3qTVwRVJskqzjYtPXHnB3fHxmfvHxqpbvtTwsGsy+f73u6D9u98NBx8MV10Vvo8o/D5/4krj5/rIiH+QG9z9ScxCFxFJsmU5F0zwk4ZAbaXjj5LJWSosKAYuiAQuQNOZVYBGxE89riIpDiR7254ad7e39LumkX68E2V+gk6U7Rx2DSa3rXRNNX+3AjYe6JpsTgQEsuOcJKvmSppsL2QWhSxggY6AhwI+9xrFK/HSt941JO1b724nsXmmSBkKdKeFF5DNZtMZqK10/HEv7Q/bz1VXwf33w/r1rizB+vUuI7Pa8gMircjLNvZTabZx3BNXcZcvVhzA3lVoADM5CXv3wumnzwS7K6nrD7UpLZLGz/V6vC5ERNqZl0nXmZ0J+KQpUFvp+CsJtoUFxTKL4Kg3K0AjUk4lweVyKjlxBfFPlPkF1O0+sBMu6L1psQuEe8tu3+AC4HdvDD9JVqyaK2mW5SATEMgGOOJsmN7l/1gSSlV5zT1PXOt+J/3/H5EAKl2SJqXN0o45xmX8JTEY4qeS8VdyaX+5/eiyekmqRtSijiqXcyVC/FSabRz3xFU1NayHhmAqpEbemWfCX/5SWV1/qF1QN22f6/V4XYiItLviZmn57S7IsjSXniBDJeOvpORJWB3ZTAf0fT49x0ykWZblXI1oP5VeRVJplnjckhnlMrMnx9y2XrwRrjt3ZptmXvS/pZoraYr/numpQlC7030+veQHsOtuGL0ivaWqRFJCge606elJd6A27vgrDaqk/ThJ+6m2FnWtZbNw8cWwYsXcxy6+uLJAbNwTV9XUsB4Zmcnk9jM15YLLldT1h9oFdUtPbrzhDckNcsNMtnnpazWTSW4WuohIGniZdGkVd/yVND9THVmR6j16E+ATLO5YWPn7qJpeA3FOlJUrXwQuwPzzM2G6qNSe3Vf+b6jVZ0nY3zORV9NHkQYw1lbZWbcF9PX12a1btzZ7GBLELwDoBVVUdkRaQT7vaknnferKZbPVNz1MypjibrOaMWzY4GpyT4Y0hFmzBj784eB9+Knl50+aP9vGxtKThS5tzxizzVrb1+xxSGNpfp8Cfs3rvEBTWNmRibH0Zr+LNNNE3tW39mvK2NkDZ91f2XspdLvZ2jVZ3L7B1eYuF+w2Xa6ciZ/MfNcMsqMbMHDsCjCmcZ8llX7uicgcQXN8ZXS3kiSVPailtF3anwat+lpJqyi1qBt9hUI9xhQ3G7ia7OFcDt7znuBAt5cRHrQPY1w2uzFwxBFunXvvrd3nT3ENcY+XVd7f35yTG3HoqhkRkcaZyLtL9vMjLnNyWc5lN6ddpSVb0p79LtIsobW0rXsvlntvBX0eNeJqi7ArQf6qMzjIDXDoKbD/Cc07SZb2UlUiKaBAd6tIWtmDWlNQpXZa/bWSRtXUoq6XWozJ74RK3BNXlZ7oymbhBz9wjSf9FJceacbJtCSe3BARkeTxy/67flXrZP8paF0brXoyRGqr0lrannKfR/UO4HoB9WvPgKkx/2UyHWDmB5dRWXpO8z9z0vK5p88VSSkFultB2jMDpXH0WkmmampRN2NM8+fDjTe68iBBVwOUO6ESJ4hb6Ymu006DK690jSenpmBiIjgjvNEn0+KeSNBVGCIi7Wci74JKxeUAvODNlv7alQOQdGv1kyFSO9XU0o76eVTvAO4hy+Hs++GWj8PtnwHMTCkSk4HlG2H4XP91VQc7On2uSIplmj0AqYEomYEioNdKUuVyLvjqp1ZND+MKG9PevfCTn8DKla6+9fDw7MeLT6h4wdzx8Zn7xwIyMOrhtNPgL3+BL37R1eRev96d0Gn21Qu9vbBokf9jpSc3hofdcV65EtatCz7uIiLSWkLLDEy7zElpb8XBRy/oODXubm/pd/XMRTzLci7Y68cYmNwNN6x2tbAnSuptJ+nzqKsHTrwYznkY/uYSOH4N9K13wfbFp7lgbGe2UIcb97sz27imtRN5dwyDjmXS6XNFUk4Z3a0giWUPJJn0WkmmampRN3JMpYKuBkhaWY4klj5auhR27fJ/rPjkhq7CEBFpX9WWGZDWFyX4mIYSCdIYQbW0sWCn4MY1wdm7Sfw8Csogb2Yd7FbIhNbniqScAt2tIErZA132LpDMEhniJK3paj4Pt98O73gHPPywG9svfgH79s1dtjR4rRMq4fJ5ODfgkkqAjRuTe9KgmP5fERGpryhlBlRDtb0lMfgoybb/CXDCv8PoFYCBw14Ov79ods1rv5Ik1ZQ9aYZm1MFulXJT+lyRlFOguxXkcq72rZ9MBo480l3mruaDUu610owSGTIjKZnHfvW1Jyb8g9wwE7z2Ap833eTqeO/dO3fZdjyhUhoQ3rMnOHjd3Q333DNzO6knDdTUVkSk/pblXCagH5OBhUfCpiXpzhyU6qQt+CjN5Zdt/OC1wct72btL3wBTe8BO+C+n2tdOq2RCLzoSzDywPt/99LkiKaBAdysIK3uwcaPLHNRl7wLJLJEhyRJWKiNIdzdYO/uEWpB2O6FS6UkDTxKvwlA5FRGRxggqM2Ay8OKNcN256c8clOqUOxmi4KN4wrKNg0yNwwPXwrYL3WfQdMn81fs8alTt66RrhUzoncNw04f8g9ygzxVJBQW6W0VQ2YNvfzv8svdLL3WZl7r0vH0krUSGJEtYqYwgxsAll4Q3mWy3Eyr5vPt8XbXKBbY9UU4aFAevk3gVRpLLqYiItJqgWrM7vh2eOXjnpZCZr5ImrS7sZIiCj8mQlPJCO4ZgOiAjO0jHIrhnI0z7XKVpuuA5n4CnnNder7Ow5zPtV1j89WRIwHe6jh59rkgqKNDdSvzKHpS77P3CC2HePF163m6SUiJDkifsM8OzYIErv+EFr9/9bhfo9jNvHrz85XDOOe1zQsXL4t63b3aQO4rS4HUSr8JIajkVEZFW5Vdrtlzm4LYLITNPJU3aQTMb70m4JDUmfPwWmN4Tbx07DXT4P5aZBx0L2ut1Vu75TPsVFmGlVzLz4TkX6/8QSQUFultd2GXv4IIwXiBGl56LpFetGgOW+8zwrFoFxx/v9vOxjwUvv28fnHBC+5xY8SvrEaSra/aJxqDgddKuwkhiORURkXYTljkIrpbuVGGOr5Imra8ZjfckXK0aE9YqI3zvI+GPm04XzCy+KmDJa2DHN/2XT0spjlqJ9Hym/AqLsBOo03th172NHY9IhRTobnVhl70H0aXnIulSy8aAuZy70iNMR4cLcnufEQp8zoha+qW7Gz7xCZcdHyV4naSrMJJYTkVEpN2EZQ4GSVMzNJG0q0VjwlpmhM/bP/zxY/4JDjhxbomke7+f3lIctRT2fE7vg+HXw5HnuM/mtF5hkfbSKyIFCnS3uqDL3uM0QxOR5Kp1Y8CbboKpqfBlSj8jFPicEaX0C7jjct556bxyJonlVERE2k1Q5qCdmNswztNuGZgizVRtY8JaZYR7OuYHP5ZZ4ILcpYH3tJfiqKVy2c73/wR2XjdzIiKNJxT1fEuLUKC7Hfhd9r57N6xZ4x+QWbTILb96tRpUiiRdLRsDekHz3bvDlyvN0lbgc0a50i/z5rkGwLU8LrUqWxNH0sqpiIi0I7/azJO74cY1/gGZzHx49EbYvkHNKUXqrdrs2FpkhHsm8jDyheDHTad/EDPtpThqqVy5KKh9mahGNzLV8y0twlhrmz2Gpuvr67Nbt25t9jAaK5+HJUuC68guWgS7ds0OVqlBpUjyrF4N69YFP/7KV8Kznx0tALphA7z3va7RZJhs1j9TfGxMgc+wz9Z58+DTn65tJrdf2Rp9ZovMYozZZq3ta/Y4pLHacn4PLjCyacnsLNBSxYELNRYTqY+w92Jntnwg9IbVcFvIHP/4NfCMD0cLhG7fAFvfD9O7/Lf19NWu0WDg3zKWzlIctRTls9XT0Q1966vL6vYrW9Ooz20935ISQXN8ZXS3K78MTC+4DTO/1aBSJNnKZRBfcw385CfR6nbffHN4kLujw31OBGUjJ6mOdLOUy26vZfC51mVrREQk/fwy8kqpOaVI/VWbHVsuI9xaF3iNUr/7wWuDg9wAxpT5W9TsNNJnq6faMlG1LlsTl55vSblMswcgTeRder5+vStjcu65LojlxyuBICLJksu5IGoQrxb/+PhMYHRszH/ZRx8N39cLXuA+M5QpHK70s3X9+voctyhla0REpP14JU361sPhrwQzz385r/yBiNRH8Xvx+DXu91mj0TJyl+VcUNyPMXDHJS4Q6gVAp8bd7S39LiPXM5GHe74XvJ/MIjUZjCrqZ2u1jRujlK0RkUDK6G53xRmYq1fPZHKXUoNKkWTyyyCePx/27vVfPqxud7na3Ced5LJHNmxobD3oNGpEdntY40t9ZouItDcvI++JO1yTND9qTilSf5Vmx4ZlhPe+2wW6/ZTW794xFBwwB2DKlaaoVT3oRteVbjTv+Vz6hkIpE5/mv9U2bqy2kalIm0tcoNsYswD4BTAfN76N1toBY8wBwBBwFHAX8AZr7Zz0Q2PMK4H1QAewwVobUmxKZgkrgVDafE5EkqO0MeCNN7pyJX6CAqD5PPzwh8H7mD8furpc/enikhzlyqFI/egzW0RSRHP8Jqm2IZ6INI9fw9mlObj5Y9EDofkRmAopW3Lkua5JbWlAPagMShi/utKVbCcN6tm4UZ/bIlVJYumSvcAp1toTgBOBVxpjTgLWAD+z1vYCPyvcnsUY0wFcApwBHA+8yRhzfKMGnnphJRAyGfe4iCSTl0G8di2cc44LdPoJCoCWK3MxMeEaKebzM4HVKOVQpH70mS0i6aI5fjOElj+oMutQROrPyyA+ca373dUzEwj1UxoIzfYSnN/YCQe/cKYedLkyKGGK60pXs500qaY0TRh9botUJXGBbut4n4JdhR8LnAlcWrj/UuB1Pqs/H9hurf2ztXYf8O3CehKFVwIhm50JknV3z9yvpmYi6VBJAPSWW8IbUU5Pu2B30GOqB914jfjMzuddqZrVq93v4saXIiIxaI7fJF7WYWd2JjDW0e1uV5t1KCLNEScQuv9zgcmADU3C7p21qQfdrnWl/U5EVL1NfW6LVCNxpUvgr1kb24BjgEustb8xxhxqrb0fwFp7vzHmEJ9VlwD3FN2+F3hBwD7eCbwTYOnSpbUcfrqVlkA45hgXFKtXkDufd/tSvV+R2vGr293d7YLcQQHQrVsr318l9aD13q+Nen5mDw/PfQ2pVI2IVKHec3zN7wMElT+oZ7Ck1ev0ijRTnLIZ154evq3bPxlc2iROPWjVla6ten9u6zNaWlgiA93W2ingRGPMfsAmY8wzI65q/DYXsI8vAV8C6Ovr812mbUVtohYUqIoawFIQRaR+4gRA83n3fqxU3HrQeu/XVj0aX3olaYozuL2SNf397rWlq3xEJKZ6z/E1vw9RaUO8YlEDI+1Up1ekWaIEQh+/A/Y9FL6dsPrdcepBq6507dXic9uPPqOlxSUy0O2x1j5mjNkCvBJ40BhzeCHT43Bgp88q9wJHFt0+Ahit/0jbUFCg6uKLYc2a8gEsBVFE6i9qAPRJT6puP3HqQeu9nw5DQ+5z3I9XqqbWwXURaRua4yecX0D70ZuiBUaK6/R6vMDXln4XmNNl9yK1US4QesVx1W0/Tj3oZTn3mVDtdqS+9BktbSBxNbqNMQcXsjwwxiwETgVuB34AnFdY7Dzg+z6r/w7oNcY8xRgzD3hjYT2ppeJAVWlTuhUrojWrixJEEZFkmz8/fj1ovffTYWRk5nO8VCWlakSk7WmOnxI7h2HTEti2Em5b535vWgzXnhatyVy71ukVaSWV1INWXel00Ge0tIEkZnQfDlxaqOGXAb5jrf2RMeZXwHeMMecDdwOvBzDGLAY2WGv7rbWTxpj3AFcCHcBXrbW3NOfPaGFhgaogpRmACqKIpFtXF3zqU3DeefEysPXeT4feXndFjt9zFbdUjYiIozl+0oVl+gXxAiNeVqnq9Iqk24EnwTHvqKwedDP6AUg8+oyWNpC4QLe19vfAc3zufxh4uc/9o0B/0e3NwOZ6jrHthQWqgpQGsBREEUmG4hIiURQ3tayknrbe++mQy7myU37ilKoRESnQHD8FwjL9gpQGRlSnVyQZdlVQ3amj2wW5q6kLXa+60jJXJQ0l9RktbSBxpUskBbxAVRylAaxczgVL/CiIItI4cUqFnHEGrF/v6mhX2jRS7/108ErSZLMzn/fd3fFL1YiISHqEZfoFKQ2MLMu5erx+VKdXpHFu/FD8dfQeTQ/fMlNL3P1h9BktbSBxGd2SAmGZfkFKA1hesKS0oaWXKaogikhjjIxEW27RIvjOd6p/b+q93xz5vDupMTLiTlbmcu65KLfsv/87GAP33utOVuZyeo5ERFpVWKZfkNLAiFent7RxpcmoTq9II+Vvj76s3qPpUmlDSS8DfMlr4J7LgA6Y3qXnX1qOAt0SX1ig6uKLYc2aaAGs5ctdZujQkCtrEhREiROgEZF4entdEHvXruBlOjrgyitrF+CM+t6X2hgenvt5vWqVf/kZv2WrKVUjIiLpsSwH1wcks3Qswl0MbMsHr6PW6a3ksnsRiSb7NHj4t+WXq6YmtzRHlIaSpeVjdg7PPQFpp+Cot8ChL9PzLy3FWGubPYam6+vrs1u3bm32MNJnbMw/UBV0fyUUdBGpr3weliwJrtX9d38HX/yigtBpFfb8ZrPuhIP33MZZViRFjDHbrLV9zR6HNJbm9xXyC4Z4Ae39T6xdk7mw/RyiOb5I1XaNwuVLwpfJLIK/+ZxqaqfNDatduZIgx6+BE9fO3J7Iu7Imkz5z/M5scAa4SMIFzfGV0S2V6+mB833+Uwy6P6583gW5i4MuXgO7/n4FXURqwe8Kja4ul8X9/e/Daac1e4RSjaEh97z6mZ52j3uf13GWFRGR1lQuG7sWAbFKL7sXkegWLYa+S2DriuBlMh2qyZxGcRtKVpIBLpJiCnRLcinoItIYKiXSukZGZk4Qlhofd893JcuKiEjr6uqpb9BDQReRxjj2AjjybPjlW+HBq2fuzyxyQW7VZE6nsDJTfg0lwxoNT427k5oiLUSBbkkuBV1EGqdWV2LUkurzV6+315V88vss7e52JzUqWVZERKRSCrqINM7Cw+DlP4WJsdqVHqqW6vNXJ27T37gZ4CIpp0C3JJeCLiLtK04DRQmWy7nj5ieTccd4wwZ3MuHII8GY4GVzurRVRERqQEEXkcar95UaUfnV579+lerzxxW16S/EzwAXSTk1o0TNahJLjdFE2pPe++HiZroHNfW9+GJYs2b2/da6n0xGDYClZagZZXvS/D7B1BhNpD3pvR+s3lnuagAsLUjNKCV9/JrkFQdd2jnQJdLKVJ8/WCWZ7n412Pv74bjj/Jv99vS4IPi996peu4iI1F7cy+5FpDWoPr+/emS5+wXOo2aAi6ScAt2SbGqSJ9J+VJ/fXz7vAtR+wen+/vBM99Ia7Bs2BJ9MsBYWLIC1a2szbhERkVJxLrsXkdag+vxzTeRdkLs4y907Rlv6K8tyDwuct+OJBGk7CnRL8iWxSZ6I1I/q8/urZaa7TiaIiEizJaVmsIg0hurzz1XrLPd6BM5FUibT7AGIiIjMksu5EkV+2rkpYi2D097JBD/tfDJBRESSaSIP2zfADavd7wmfGr8ikmzLcq5EkZ92bYpY6yz3KIFzkRanQLeIiCSLV58/m50JxnZ3z9zfrqWLahmc1skEERFJi53DroHdtpVw2zr3e9MSd7+IpIdXn78z6zK4wf3uzLZvfX4vy91PJVnuKg8jotIl0kLyeXfp/siICwjlci4wJiLpo/r8c+VyrvGkn7jBaTX7FRGRNNBl+CKtRfX5Z1uWc/Wz/VSS5a7yMCIKdEuLGB6eG7BZtcoFbJZX2KlYRJpL9flnq3VwWicTREQk6Wpdv1ZEmk/1+Wd4We6lzSNNprIs91oHzkVSSIFuSb983gV+8kWZHl4d2/5+F8hR4EZEWkGtg9M6mSAiIkmmy/BFpNXVMsu91oFzkRRSoFvSb2jIZTf6mZ52jyuQIyKtQsFpERFpF7oMX6R9TOTdVRz5EffeX5Zzgdt2UMssd5WHkTanQLek38jITAZ3qfFxl/UoIq1D9fhFRETagy7DF2kPO4fnZiFfv8plIR+iUqSxqTyMtDEFuiX9entdnVq/YHd3t7u0X0Rag+rxi4iItA9dhi/S+tR0VkRqKNPsAYhULZdzzdj8ZDLucRFJv+J6/N6JrfHxmfvHxpo7PhEREak97zL8vvVw/Br3+6xRZXmKtIooTWdFRCJSRrekXzbrsjlLszwzGXe/GlGKtAbV4xcREWlPugxfpHWp6ayI1JAC3dIali+H0VEX6Nq+3ZUryeUU5BZpJarHLyIiIiLSWtR0VkRqSIFuaR09PenJ5lQzPZH4VI9fREREkmwi78ow5Edc8G5ZztUZF5FgajorIjWkQLe0j6QEl9VMT6QyuZx7r/hRPX4REZH2lJTg8s7huU0zr1/lmmaqnrhIMDWdFZEaMtbaZo+h6fr6+uzWrVubPQypJ7/gslfDu5HB5Xwelixxv0tls678isqtiARLyns5iZJyMk8kYYwx26y1fc0ehzSW5vdtwi+47AXHGhlcnsjDpiUw6TPH78y65pkK1omEmxhzjSfz2125kqU5vW8gOSfzRBImaI6vjG5pffm8C4wVB5e90gf9/Y0NLquZnkh1Wq0ef62C07pSRERE2s1E3gW5i4PLXo3fLf2NDS7vGHLBdj922gXv1ExTJFwrNJ31gtKP3wJ7H4F5+8N+z6w8OK0rRURiU6BbWl+SgstqpidSvTTV4w+Sz8NHPwqf+5y7PT0NixZVFpwOO5l36qlwwQVw/PHK8BYRkdaSpOByfsS/kR64+/Oa44u0vNGr4BevhekJoOizycyvLDgddjLvmlOh9wJ48vHK8BYpkWn2AETqLknBZa+Znh810xNpD8PDcOih8NnPugC3dyJu166ZoPXYWPTthZ3M27sXPvMZWLnSlU0aHq529CIiIsmQpOByttdlWvrp6HZlGESkdd1/FWw5Hab3MivIDWD3umD1ln5XniWqsJN503vhj5+BbStd2aSdmuOLeBToltaXpOByLufqCftRMz2R1pfPwxlnwO7dwctMTbngdVRhJ/M84+OVBdFFRESSKknB5WU5Vxvcj8m4WsMi0pom8vDzM8svNz3lrjSJKuxknmdqvLIgukgLU6BbWl+SgsvZrCtLkM3OBN+7u2fuT2udYRGJZmgIJibCl9m1K96VJmEn80p55ZpERETSLknB5a6sK0vQmZ0Jvnd0u9snb1ZDPZFWtmMI7FT55aZ3xbvSJOxkXimvXJOIKNAtbcAvuLxoESxYAK95DXz727Nr29ab10xv/XpYs8b9Hh1VwziRdjAy4sqJhMlk4Igjom8z7GReKfUCEBGRVpG04PIhy10DzL71cPwa9/usUTWME2l1+RGwZRJZAOiMd6VJ2Mm8UuoFIPJXakYp7cELLg8NwbXXwsaN0NEB3/wmfP/7lTWA85PPu32MjLgsy6Dmb63QTE9E6mN62p0EO+GEaJ9J3sm8/n63blgZE/UCEBGRVuIFl+8eckGe7DEuk7vWQe6JvMvazI+4LMug5m9dPY1rgCkiyZDthY5FMLUrfDkDLO6Pvl3vZN6WfpexHVbGRL0ARP7KWGubPYam6+vrs1u3bm32MKQR8nnXkM0vgzubdcHwSsuHDA/PDjR1d7ssy1oE0EUk/fJ5WLw4eo3suJ9JY2PuRNstt8AXvwh79lS/zWJRT+SJJIwxZpu1tq/Z45DG0vy+TUUNSMexc3h2oKmj22VZnrxZ2doi4j53Ni1xtbLDmPnQMS/+Z8fEmDuZ99gtMPJFmPaZ43dm3Um/uCf56vGZKdIgQXN8ZXRLexkacoFoP17t2koyrb0mb8UBdC+rsr+/ugC6iLSGoSGIc3I57mdS8ZUiZ58dfOKtks8ivxN5tboSRkREpBb8AtLXr6ouID1RaPJWHMDysiq39FcWWBKR1lKceT094R+IBrB7YXJv/M+O4itFjjw7+MRb3M+ienxmiiSAAt3SXkZGgi/rr6Z2bb0C6CLSOsI+f/yMj8OXv+yC43Gzp4vLNW3f7sqV5HKVZ3LrRJ6IiCRZvQLSO4ZcEMiP1/xNpUpEpLiM0uO3wkO/god+B0zOXXZqD2xdAX2fj589XatyTTqJJy1MzSilvfT2zjSkLFVN7dp6BdBFpHWEff4E+c1vYOVKV3JpeDjeul6G99q17nelwegoJ/JERESaKUpAuhL5keC6uGr+JiKzWJegYjqh80n4BrnBNa6885uu3MnOmPN7mMnwPnGt+12vk3giKaVAt7SXXM5dvu8nk3GPV6JeAXQRaR25XHDAOMz4+ExWddT63rWkE3kiIpJ09QpIZ3vd5fx+1PxNRDw7h13gettKuG0d7NxSZoVJl029pd/V4G40ncSTFqZAt7SXbNbVlM1mZwLT3d0z91ea8VivALqItBZjgh9btCh83eLs6XweNmyA1avdb78Gu7WiE3kiIpJ09QpIL8u5+rd+TMaVDBCR9lZcBsQLHk/vjbZuafb0RB62b4AbVrvfE3Wa4+sknrQwBbql/Xi1a9evhzVr3O/R0eoaqtUrgC4irWNoKDjQ3d0Nr389vOAFwet72dPDw66UycqVsG5d5aVNotKJPBERSbp6BaS9JnOd2ZmgUEe3u11J8zcRaT1hZUAA6Ah+aGoc7t7oAtqlWeHbVlZe3qQcncSTFqZmlNKevNq1tVTL5m8i0nrKlQA5/HD3OfKHP8CuXXOX6e6GI45ofGNI74Rdf7/LKh8fd2PJZHQiT0REksELSG/pdwGnqXEXkDaZ6gPStWr+JiKtKawMCMCTnw6P305gze4HroFNi8FOwdTumfvr2Ryynp+ZIk2mQLdIFPm8C2CPjLjL+HM5F/wpVY8AuohULup7txG8EiB+wW6vBMiRR/oHuQGmpmDPnvKNIevxGaQTeSIiknT1DEh7zd9EJBkm8i6TOj/iynAsy7ngbTN4ZUD8gt0d3dD7T3DDP8NUUHPKfTC5L3j7XnmTWn8G6SSetChjrW32GJqur6/Pbt26tdnDkKQaHg7OZKym3ImI1FfS3rv5vCsx4ldPO5uFP/4RjjsuuN72/Pmuk/u+kInwmjWwdm1txisSJEknkCIwxmyz1vY1exzSWJrfi4i0qJ3DwZnIhzRhjj+RdyVGJn3m8J1ZeM0f4QfHwFRAMksUx6+BEzXHlzpK0smjiILm+MroFgmTzze+TICIVC+J791yJUCuuCI4Wxtgb5mmNmoMKY3gdwJp1Sqd/BUREZH6K2786KlniY8oypUBue8KIKQhfTlqDin15nfy6PpVzTt5VCUFukXCDA01p0yAiFQnqe/dsBIgP/xhcA3vKNQYUuotiSeQREQqlcLsNZG2F9b4sV4lPqIIKwNy3w/Da3iXo+aQUk9JPHlUJQW6RUoVX5J9003hzeO2b2/s2EQkmnKNH5v53g2q5R9WwzvMokXQ0aHGkFJ/ST2BJCISV4tlr4m0jbDGj1PjLsjcLEG1/MNqeBczHcB8yBg1h5TGSerJoyoo0C1SrPSS7Pnzg5dVmQCR5IrS+DFpcjlXAiKOri54/evh859XkFvqL8knkEREyvEyuB+/BUa+CNNFJcFSnr0m0jbKNX5MYomPZTl3Iq0cOwXsgiPfAouOVHNIaYwknzyqUKbZAxBJjOJLsr0v8mE1cVUmQCS5cjn3HvWT1PeuV8M7m3VZ2lFMTMDhhyvILY3hnUDyk9QTSCIi4DK4Ny2BbSvhj5+dHeQu5mWviUgyLcu5LGc/SS3x4dXw7sy6YDxAx8Lg5e/6Bhz3fpdFqyC31Jt38shPUk8elaFAt4gn7JJsgHnz3O/u7pmAlIJLIslUHDT2AnNpeO96Nbw/9zl4y1ugs8yFV11d1QcX83nYsAFWr3a/8z4d40UgnSeQRESK64+WKx2Q0uw1kbbhGzTudreTXOLDq+Hdtx6OXwP7nRi+/I1rKt/XRB62b4AbVrvfE5rbS4g0njwqQ6VLRDxhl2QDvPzlcMIJs5vHiUhyFTd+vPVWeOghOOAAuP12917OJrThlFfD+/zz4d574ec/D152aqq64GJpuabublc+ZfNmd/xEinknikpfM5lMsk8giUh7C6s/Wiql2WsibSWs8WOSFdfw/vFzwpd97A+V7UP9ByQu7+RR6esmxfXhFegW8ZSr6XvOOWqyJZI2PT1w3HFw4YXpDOY+9FD440cdVXlwsbhck8f7/OvvdycJFLiUUsUnkLZv18lfEUm+sPqjpVKavSbSdoIaP6bFvifCH594PP42i69e8aj/gESR1pNHAVS6RMSjS7JFWo9f7f3x8Zn7x8bqu+9qS4Icfnj440cfXdnYILxc0/S0e1ykmPea/tjHwFr48IfdCWAFuUUkycLqj3rSUPpARJKhFqVB5j0p/PGuJ8ffZtjVK+o/IGEm8rDj2/DEHdBzNCx9Q6r/L1RGt4hHl2SLtJ4owdx6XKlRq5IgZ54JV18d/nilwso1jY+7bF0Rj8rciEhaLcu5S/f9ZBbAsRfAk49PdfaaSFubyLsgb37EndhalnPlGOqhVqVBnvxsePTG4Mf3e1b8sYVdvaL+AxKkBcvdJC7QbYw5Evg6cBgwDXzJWrveGDMEHFdYbD/gMWvtiT7r3wXkgSlg0lrb14BhS6vQJdkiraUZwdxalgQ57zyXEb5r19zHFi1yj1eqXLmmaptcSutQmRupAc3xpWnK1R9N6Rd5EaGxQbpalgY5cS3c9fWQxy+OPz7v6hW/YLf6D4ifFi13k8TSJZPAB6y1TwdOAlYYY4631uastScWJr6XAd8L2cbLCstqAizxeY3g1q7VJdkiaecFc/3UK5hby5Ig2SxceaX7HJo/3903f7677d1fKZVrkqhU5kZqQ3N8aR6v/mjfejh+jft91qiC3CJpVhyk84JzU+Pu9pZ+mKhxicJalgZZtBj6LvF/rO8SWHhY/PEty7kTeH7Uf0D8hL2mp/bCnZc2djw1kriMbmvt/cD9hX/njTG3AUuAWwGMMQZ4A3BK0wYpIiLpkMu58gp+6hXMrXUW+fLlcP/9tb/SROWaJCqVuZEa0Bxfmi7tzetEZLYogedavudrXRrk2AvgyLPhxjXwxB/hSce5TO5KgtxQ/uqVFGbmSp2FvabtPnd1xH4npO6kcOIC3cWMMUcBzwF+U3T3i4EHrbUjAatZ4CpjjAX+01r7pfqOUkREEqsZwdx6lATxrjSpNZVrkijCXtMAe/Y0djySeprji4hI1Rpdk7oepUEWHgYv/FrVQ/sr7+qVu4fc3589pjb9BxpZB10aJ9vrelVMB8zlp/elsoSJsdY2ewy+jDE9wM+Bf7PWfq/o/i8C2621nwpYb7G1dtQYcwjwU+C91tpf+Cz3TuCdAEuXLn3ejh076vFniIhIEoyNNS6Ym8/DkiWz6xl7slnVM5b0yedh8WL3Pgpy5ZVw2mmNG1MMxphtKnWRHPWc42t+LyLSRrZvgG0rgwPPfetrm9E9kYdNS2bXM/Z0ZlMXDIzMrw66ehy0hl2jcPmS8GXq8V6qkaA5fhJrdGOM6cLV6PtGyQS4EzgbCCx+ZK0dLfzeCWwCnh+w3JestX3W2r6DDz64lsMXEZGkaWTtfS+LPJudqQ/e3T1zfxqC3Pk8bNjgGmFu2OAftK/lepJs2SxccEH4MmeeGR4IF6H+c3zN70VE2kija1J7pUE6sy74B+53ZzY9pUEm8u4EwQ2r3e+JMnP1RtdBl8Ya3ewyusPU4+qIOqtZ6ZLBwUEDHAgsGhgYuLvS7RTq830FuM1a++mSh08FbrfW3huwbjeQKdT96wZOA/5PpWMRERGpSJpLggwPzy31smqVC9IvD8naqHQ9aQ1TU+71Xo8SO9JUmuOLiEgiBdakNtD7brj5Y7Uvs1Gv0iCN4JeZff2q8MzsRtdBl8bKjwSXLfFUWpaniaoOdA8ODj4X+AhugtqNq5/XWfT4/sDFhfvfNzAwsK/MJl8EvBX4gzHmxsJ9H7bWbgbeCHyreGFjzGJgg7W2HzgU2OTm0XQC37TW/qSqP1BERFpbPu8CdCMjrhZxLucyWKtVr7ra9ZTPu2B1cSa2V5e5vz+47Eql60l6HHlk+OMTE2pK2WI0xxcRkcQrDTxbC3dc4n6mxiEzH7augONWwjM/UpuAdxob2xZnZnu8DO2wGsyNroMujZXthY5FMLUreBk7CY/d7K4ASElt9qpKlwwODr4V+BXwOqAHMIWfvxoYGHgUeArwj8Arym3TWjtsrTXW2mdba08s/GwuPPZ2a+1/lCw/WpgAY639s7X2hMLPM6y1/1bN3yciIi3uqqvg4IPh3e+Gdevg/e939bWHh5s9suYYGnIZ2X6mp93jtVxPWse8eZU1WpVE0hxfRERSwws8P+PDMPIFmBqbCc5O73UN9W5bB99b7LKa21GUzGw/XgNOPynM9JUSy3LBrwvP9BT88bOuHv6mJal4D1Uc6B4cHHw68GWgC/gc0Ac8FLD413GT4zMr3Z+IiEhNXXUVnH467N0Lk5Puvl27ZrKT27He8MjITCZ2qfHx4IzdSteT9LjnnvLL5GpcC1OaQnN8ERFJpbBgLrgAeLvWla40M7vRddClsbqycOTZZRYqfE9OUW32ajK6VwHzgEsGBgZWDgwMXA9MBSx7TeH3C6vYn4iISG3k8655XpDdu2HFivZrptjbO9NAs1R3d3DGbqXrSXqEPccA//iP8O1vqxFpa9AcX0RE0icsmOsJy15uZZVmZrdCA04Jd+jLILMo+vJ2KvHvoWoC3afgavJ9otyCAwMDo8AuYGkV+xMREamNSy+FfSHlZCcn4ZvfDC5jks+7YF6rBfVyOcgETA0ymeCM3UrXk/QIe44BLrkE3vteVwJo5cr2LgGUfprji4hI+mR7ywfspsbhsVsbM54kqSYz26uD3rcejl/jfp81GtzAUtJlWQ4yHdGXn9oFD1xbv/HUQDWB7sXA+MDAgG93dB+7gYVV7E9ERKR6w8Nw4YXBNaU9k5P+ZUyGh10Qb+XK1gvqZbOwebP77WXvdnfP3B/UULLS9SQ9ojyXewpd28fH27sEUPppji8iIunTvRSmQ5rqeUYuSUWd4ZqqNjPbq4N+4lr3W5ncraMrCy/8erx17v5uosuXVBPo3gvMGxwcNOUWHBwcXAjsBzxexf5ERESqk8/DGWfM1OSOoriZohe8y+dnalK3WlBv+XIYHYX162HNGvd7dNTdX4/1JD2WL4e1a2H+/GjL5/NqRJpOmuOLiEi6TOThunOiLTu9NxV1hmtOmdniZ+cw/O8b461j98Gdl9ZnPDXQWcW6dwHPAnqBO8os2w90AG14jYiIiCTG0BBMTMRbp7iZ4tBQcCa4FxA///zqxpgEPT2V/R2Vrifp8ac/uQauUV1+uV4T6XMXmuOLiEia7BiC6RhzfK9W99Elc5SJvNtWfsSVQlmWcxmvrcLLzBYB93rf0u9O/sQ18p9w7Iraj6kGqsno/gmuy/r7wxYaHBw8EFiHq/V3RRX7ExERqc7ISLwgHcxupjgyMpPJXao4IC7SioaH4YtfjLfOFZr6pZDm+CIiki75kXjBuqlxyJfM23cOw6YlsG0l3LbO/d60pP3KnEj72DHkTvpU4vE/1HYsNVRNRvdngAuAfxocHNwJfLr4wcKljGcB/wYsAx4C/qOK/YmIiFSnt9eVXYgT7C5uptjb6wLffsHu4oC4SKvxyvPEPVFkbX3GI/WkOb6IiKRLthcy86MHuzu6IVs0b/cyWyeLGsxPFeb7W/pdiQ/VpZZWkx+ZeZ23kIozugcGBh4E3gxMAB8F/gIcCDA4OHgL8Ajw37gJ8F7gTQMDA09UO2AREZGK5XLQ1RW+zKJCt/biZorWwoYNcMstwfW9iwPiIq0mrGxPOb//fW3HInWlOb6IiKTOshxkyszxi5kMLO6H7RvghtXwu/fA9JT/sl6ZE5FWk+2daU7aQqopXcLAwMCPgJcA24B5uAxxAzwdmF/49w3ASwYGBn5W3VBFRESqlM3Cj388E8wutnAhXHklfO5zs5spAixZAitXwmc/C6bQn23BAve7OCDeo0wPaVFhZXvKOfvs2o5F6k5zfBERSZWuLJz8Y+jwmeMDZArz9o5u6MzCiRfDD4+bKVOy41swvct/Xb8yJyKtYFnOnfRpMdWULgFgYGDgt8DzBwcHnw0sBxbjmtI8APzvwMDA1mr3ISIiUjPLl8ODD8Kll87UD37Vq+C88+YGqr1yDfmiyxj37Jn596pVcPzxLpNbQW5pZWFle8p54IHaj0fqTnN8ERFJlUOWw9kPwp2Xwn2FOf6SV8GR58DoFS5YnT3GZXL/8LjZZUpsSCPL0jInIq2iKwsnb3bleey0O6nT0e2C38Xvj5QxVrUT6evrs1u3aq4uIiIlNmxwmdxBNbnXr4fz1blc2kA+765syFcw6T366KY2ajXGbLPW9jVtANIUmt+LiIiv7RtcJnfU2sSdWdXoltY2MebK83gng5bm4LvZ8uu9ubnx5KA5fuvlqIuIiNRKWLmG8fGmBu9EGsorz7NwYfx1/+d/aj8eERERkUqUbcBXKHzglTk5ebOC3NLaunrg6PPhGR92valu/lizR1SVqkuXiIiItKywcg3d3XCMLmOUNnLCCdDREX+9W26Bk06q/XhERERE4vIa8PkFuzu6Yem5sPDwmcxWBbmlHewcnl3CpKzk5k1HCnQPDg5eU6P92YGBgZfXaFsiIiL1lcu5Otx+Mhn3uEi7GBpyWR5x6cqHxNIcX0RE2s6yHFwfML83Gej7vILb0l4m8i7IHacu90EvrN94qhQ1o/vkMo9733pMwP3eYyoILiIi6eGVa+jvh+lpl9nd3e2C3Js3qwFlXPm8C5aOjLhs+VzOHWNJh7BSPmGOOKL2Y5FaObnM45rji4hIawlrwKcyJdKOdgy590IcnYvqM5YaiBroHgy4fx7wbmA/4D5gC3Bv4bEluMnzEcCjwH8A+yobpoiISJMsXw6joy5Au327K1eSyynIHdfw8NwTBqtWuRMGy5c3e3QSRW+vq9G9e3ezRyK1ozm+iIi0n0OWuwaTpQ34FOSObyLvAqX5EVcWZlnOnUyQdJjIw90bozdn9VRylWeDRAp0DwwMzJkEDw4OdgJXAwuBdwEbBgYGbMkyBjgf+H/A3wKnVjtgERGRhuvpgfPPb/Yo0iufd0HufNHlcF5mcH+/O5GgEwfJls/DlVdWFuT+859rPx6pCc3xRUSkbXkN+KRypXWdO7pdWZiTN7uTCZJs3vM3WcH8/sGfw8RYIk8OVdOM8kLgxcCKgYGBL/stUJgUbyhMmC8prPPJKvYpIiIiaTM05DK5/UxPu8d1IiG5rroKXvMa2Fdh0u7DD9d2PFJvmuOLiIhIOL+6zl5W8JZ+lzGfwCCoFEzk4dozYGqssvWNdVdEJPBkUTVtMt8CTAL/FWHZ/wKmgL+rYn8iIiKSRmG1ncfH1awwya66Ck4/vfIgN6gOe/poji8iIiLhwuo622kXBJXkuvnjlQe5AewkjF5Vu/HUUDWB7qOBsYGBgb3lFiwsky+sIyIiIu2kt9fV5PbT3e3qnkvy5PNw5pnVb+eRR6rfhjSS5vgiIiISLj8SXNd5atzVPpdkmsjDHz9b/Xbu/Z4rX5Iw1QS6J4H9BgcHl5RbsLDM/oV1REREpJ3kcpAJmHJkMu5xSZ6hIZiaqn47t99e/TakkTTHFxERkXDZXleT209Ht2vwKcm0Ywgw1W/HksjM/WoC3VsLv6PU4/OW2Rq6lIiIiLSebBY2b3a/vczu7u6Z+9WIMplGRmBiovrt3HADjCUv20MCaY4vIiIi4ZblwASEFE0GliqRJbHyIzBd9sK9CCYTmblfTTPKTwMvB94wODh4CPBxYHhgYGACYHBwsAtYDvwr8DJcrP/T1Q1XREREUmn5chgddVnC27e7ciW5nILcSdbbC4sWwa5d1W9LDUfTRHN8ERERCdeVhZM3u8aTdtqVK+nodkHukzerEWWSedn4QaVnososSmTmvrHWVrzy4ODgRcBHcRNccJctPlS4fTAukO7lw398YGDgoxXvrI76+vrs1q1KRBERERH5q3welixxv6u1Zg2sXVv9dipgjNlmre1rys5TqhXm+Jrfi4iINMDEmCtfkd/ugp5LcwpyJ91EHjYtgckq5/idWThrtGnPd9Acv5rSJQwMDFwEvA64HTfZ7QIOBxYX/m2A24CzkzgBFhERkYTK52HDBli92v2uRbBV4vFKy8yfX912Fi1Sw9GU0RxfREREIunqgaPPhxPXut9hQc+JPGzfADesdr8nNL9viq4sLN9Y/XYSmrlfVUZ3scHBwWcBfcAhhbt2AlsHBgb+UJMd1JEyPkRERBJkeBj6+2F6GsbHXT3vTMYFXZcvb/bo2s8nPwn//M+Vr9/TA/ff37QyNcrork5a5/ia34uIiCTIzuHgMieHaH7fcNs3wLaVweVLypU26ToAXv9wXYYWVdAcv5oa3bMUJruJnvCKiIhIwuXzLshdnME9Xphk9fe7Ot+q691Yf/lLdev/4z/qOUsxzfFFRESkKhN5F+QuLpXhBVG39De1/EXbyo+EB7LnHwC7Qh6feMyVrUng81ZV6RIRERGRmhoacpncfqan3ePSWEceGVy+ZNEiMMb/Mc/tt9d+TCIiIiKSDjuGXCa3HzvtanxLYy06EjLz/B/r6IaDX1xmAx2Jfd4U6BYREZHkGBmZyeAuNT4O27c3djztbngYPvQh2LvX//GODnjuc8O3MTlZ+3GJiIiISDqEZQ9PjbtGltI4O4fhhn+B6X3+j5sMPPeTYMKKgEwk9nmruHTJ4ODgnytYzQ4MDBxd6T5FRESkxfX2uprcfsHu7m41NWwkr4zM2Jj/4z09rm76lVfCtm3B27nmGhcwV331VNAcX0RERGoq2xtc87mjG7Ka3zfMRB62nAHTu4OXefFGWHg4LHsT3PXfwcvtfqD246uBamp0HxVxOYvrzO79W0RERMRfLgerVvk/lsm4x6UxwsrIzJsHF1/sgtcXXBC+nakp1VdPl6MiLqc5voiIiJS3LAfXB8zvTQaWan7fMDuGYHoiZIFO2HWP++cDV4dv686vQd//S1yd7moC3X9f5vEnAy8AzgbGgI8Cu6rYn4iIiLS6bNZlCff3uyDr+LjL5M5k3P0KlDZOWBmZffvg3nvdv2++ufy2vPrq559fu/FJvWiOLyIiIrXTlYWTN7vGk3baZXZ3dLsg98mbExcobWn5EZgOKEkIwCSMXgVHnw977i+/vbuH3LIJUnGge2Bg4NIoyw0ODj4d+CnwFuDkSvcnIiIibWL5cpf9OzTkanIfc4zL5FaQu7HCysjMmwdHHOH+bSMk86q+empoji8iIiI1d8hyOGvUBUbz2125kqU5BbkbLdsLmfnhwe57vgOjEYPXCazTXfdmlAMDA7cBK4C/BVbWe38iIiLSAnp6XPbv2rXut4LcjZfLuUx6P/v2wZo1rvZ2FKqv3nI0xxcREZFYunpc9u+Ja91vBbkbb1kOMl3ll/vFa6NtL4H11ese6C64AtgHvLVB+xMREZFWlc/Dhg2werX7nc83e0StySsjE3SSYWwMzjgj2rZUX71VaY4vIiIikhZdWTj5x5Qt8DG9L9r2ElhfvSGB7oGBgUlgL6Bu7CIiIlK54WFYsgRWroR169zvJUuiZxZLPMuXw9veFvz4roilmS++WFn5LUhzfBEREZGUOWQ5PO8zhIeEo/QZz8CjN9ZmTDXUkED34ODgsUAWCGvtKSIiIhIsn3dNKvP5mbrR4+Mz94+NNXd8rSafdycSvvCF4GWmp6Nta/VqPT8tSHN8ERERkZSZyMPUHiDiPD7QtGswOpGsOX7dA92Dg4NLgK/hTgdsrff+REREpEUNDQUHVqen3eNSG8PDsHgxrF9fm+3t2aPnp8Voji8iIiKSMjuHYdMSuOlfa7M9O+0ajCZImaIswQYHB79aZpEFwJHA3wBepfNPVro/ERERaXMjIzOZ3KXGx2F78rp+p1I9MuQnJ+HWW2u3PakbzfFFREREWtBE3mVgT9awv9HUOOST9R2s4kA38HZcBoeJsOwYsHJgYODKKvYnIiIi7ay3F7q7/YPd3d1wTPK6fqdSWOZ8qd5edwIiiocfrnxM0khvR3N8ERERSYKJPOwYgvwIZHthWc41VJT4dgy5DOxyMotgOmIfHoBFR1Q+pjqoJtD9dcKrk08CjwI3AT8cGBh4oop9iYiISLvL5WDVKv/HMhn3uFQvLHO+1C9+AYcfHm3ZAw+sfEzSSJrji4iISPPtHHYZyHbaZQ53dMP1q+Dkza6hosSTH3HHsRzTkHaOdVNxoHtgYODtNRyHiIiISLhsFjZvdmU1pqddMLa72wW5N2+Gnp5mj7A1hGXOF5s/H664Ivp29+6tblzSEJrji4iISNP5ldnwgrRb+uGsUejS3D+WbK87WVA22D0Nh/TDzs3Rtrvr3qqHVkvpDtOLiIhIe1m+HEZHXZPENWvc79FRd7/URi4HJkLVir174dpro2/3P/6jtnW/RURERKQ1hZXZSGADxFRYlouWrT21Cx65Lvp2E1a6pOJA9+Dg4FcHBwc/HWP5dYODg1+pdH8iIiIigMvcPv98WLvW/VYmd21ls3DBBdGW3bgRnvSkaMtOT7v635JomuOLiIhI04WV2UhgA8RU6MrCiRdHWzZKLe+Eqiaj++3AG2Ms//rCOiIiIiLSCjo64LnPjbastbBdX0pS4O1oji8iIiLN5JXZ8NPRDVk1oY9tIg83rom27NQ4dEXsr9PGpUuidG4XERERkWbz6nSXs2sXLFwYfbtHJOvSRqkJzfFFRESktsLKbJgMLFUT+tjCysGU6uiGg06KtmyrlC6pwEHArgbuT0REREQqEbVON8Bxx0VfVlqR5vgiIiJSW11ZOHkzdGZnMrs7ut3tkzerEWUlwsrBlJreB9MRG8lfvxp2Dlc+rhrrrPcOBgcHnwy8A1gE/KHe+xMREZEUy+ddHeeREZdVnMu5mtHN2k678up0r1tXftkvfhGOPx5uuaX8sr/6FaxYUf34pOk0xxcREZHIJvIuozg/4sqSLMu5YHaYQ5bDWaOu8WR+uytXcng/jF4B9/0w+nbE8crBRAl226no250ehy397rlKwAmIyIHuwcHBAeCjJXcfOjg4GPWvt8DGqPsTERGRNjM8DP39rmnh+LgrnbFqFWzeDMuXx9vOGWfAxATs3Qvz58OFF8KPfxxvOxLN3r3wxz9GW3bjRviP/1AD0QTRHF9ERETqauewC4TaaRdk7eiG61e5zOxDyszNu3rg6PNdoPzmj8Nv3w1YsJOQmQ/XXwgn/7j8dsSdFLh+VcSFp2HiiejbttPuhMTR51c0tFqKW7rEFP3YktthPxPAV4GI7T1FRESkreTzLsidz7sgN7jf3v1jY9G3c/rpbvm9hcvt9u51t737JZreXtdsMorJyWjLdXS4THtJGs3xRUREpPYm8i7IPZmfySSeGne3t/TDRIS5+c5h2LQYblsHdsIFucGV1pgcg2tPj7addueVg8ksiLb8o9dH3/bUuMu6T4A4pUu+Bmwp/NsA1wCPAOeErDMNPAHcMTAwsLuC8YmIiEg7GBpymdx+pqfd4+dHyBC49FLXINHPrl3ucZXOiCaXg/e8B6YiJvYuWADWwr597refXbtgezImwfJXX0NzfBEREamHsAaIUbKA/xooDwlkT+2COy+FYzXHL+uQ5XDmn92Jg3JsxEQWcFn62WMqH1cNRQ50DwwM7AB2eLcHBwfvBh4cGBj4eT0GJiIiIm1kZGQmk7vU+Hj04Ojll4c//v3vK9AdVTYLP/iBy4SPYs8eFxy/7LLgDO8FC+CYZEyCxdEcX0REROomrAFilCzgsEB5sXu+r0B3VAsPh/mHwt4Hoy2fWQDTE0BI8ovJwNJcTYZXrYqbUQ4MDBxVw3GIiIhIO+vtdTW5/YLd3d3Rg6N//nN1j8tsp50GS5bAffeVX3bRIneiIayMyZ49cMMNNRue1J7m+CIiIlIzYQ0Qo2QBhwXKi+26u7LxtatDT4G7vxVt2ek9EbZ3aiIaUUL8Gt11Z4w50hhzrTHmNmPMLcaY9xfuv8gYc58x5sbCT3/A+q80xvzRGLPdGLOmsaMXERGRiuRykAmYlmQy7vEoRkfDH48SsJXZVq6Mttz0dLSa3pdcAn/6U1VDkvTRHF9ERKQNLcu5bF8/UbKAs72u6WQ5+x6LPbS29qyLaru9+zbBXcnowxMpo3twcHBp4Z8TAwMD95fcF8vAwEC50yyTwAestdcbY7LANmPMTwuPfcZa+8mgFY0xHcAlwCuAe4HfGWN+YK29tZKxioiISINks7B5s2s8OT3tMru7u12Qe/Nm6ImYIVCuKeLERPVjbTdvfjOsXh1cQx3c83PqqeVLx3je8hb49a9rMjypnOb4IiIiUldeA8Qt/a4EydS4y+Q2GXd/uSzgZTnYGqEkSZx60gJ/+QWYLtfcs1Z++SZY8qqmZ3ZHLV1yZ+H37cAzSu6Lw5bbp7X2fuD+wr/zxpjbgCURt/98YLu19s8AxphvA2cCmgSLiIgk3fLlLiN7aMjV5D7mGJfJHTXIDS44/sQTwY/H2ZbA8LA7+ZDJBAe65893y3znO9G3e9tttRmfVEtzfBEREamvQ5bDWaOu8WR+uytXsjQXLSDalYXjVsJt68KXW3BoTYbaNvIjtQ1yA2DLNxdtgKilS0zRj999UX9ilUoxxhwFPAf4TeGu9xhjfm+M+aoxZn+fVZYA9xTdvpeACbQx5p3GmK3GmK1/+ctf4gxLRERE6qWnB84/H9audb/jBqbf977wx6OW4RDI510AO58Pz5TfuzdekBvAmPLLSCO01Bxf83sREZGE6upxAdAT17rfcbJ+n/kRMPPCl2lycDV1sr2uyWStlWsu2gBRJ6VPKfyc6nNf3J9IjDE9wGXASmvtE8AXgaOBE3HZIJ/yW83nPuu3fWvtl6y1fdbavoMPPjjqsERERCTJ/uVfoDMgsbSzEz74wcaOJ82GhmAqpLt6NZ7xjPLLSCO01Bxf83sREZEW1JWFl/4w+PHMfDjmnY0bTytY3B+tyWRc5ZqLNkCk0iUDAwM7otxXK8aYLtwE+BvW2u8BWGsfLHr8y8CPfFa9Fziy6PYRQJmuVCIiItIyslm49lo4/XTYvRusddnDCxfClVeqdEkc114Lu3bVZ9tHHVWf7UosmuOLiIhIKiw+DV52Jfz8tTC9D7BgOqBjIZz846bXhU6de75X+xrdUL65aANErdHdMMYYA3wFuM1a++mi+w8v1PYDOAu42Wf13wG9xpinAPcBbwTeXOchi4iISJIsXw4PPlhdre92l8/DZZfVb/uHHVa/bUsiaY4vIiIiVTn8NDjnocpqfcuMncNw/araB7kXHJaI5yJxgW7gRcBbgT8YY24s3Pdh4E3GmBNxlyneBbwLwBizGNhgre231k4aY94DXAl0AF+11t7S2OGLiIhI03m1vj35PGzYACMj0NvrAt/ZbPPGl3RDQ9DRUb/tH398/bYtSaU5voiIiFTHq/XtmcjD9g2uuWK2F5blXKkT8TeRhy39dWhECRx6Su23WYGKA92Dg4MvibnKHuAx4E8DAwOBBR+ttcP41+HbHLD8KNBfdHtz0LIiIiLShoaHXVPF6WkYH4fubli1CjZvdtnfMtfISP3KlgC86lX127ZURXN8ERERSYWdw4Wg7TRMjUNHt8tUPnkzHKI5vq8dQ+541cNz/dqsNF41Gd1bCGj0WMbewcHBq4F1AwMDw1XsX0RERCRcPu+C3Pn8zH3j4+53fz+MjqqkiZ/eXndCwDtWtTR/PlxxxeyMe0mSLWiOLyIiIknmZSZPFs3xpwrz1i39cNZoIspoJE5+ZOY41dJBL4KFyShNmKlyfVPBzwLg1cCWwcHB91e5fxEREZFgQ0Muk9vP9LR7XObK5WAqIDm3s9P9VGrvXlc7XZJMc3wRERFJrrDMZDvt6njLXNleMPMDHuwo/FTg4d+6DPsEqDjQPTAwkAFeCzwK3A6cDxyNm+QuKPz7fOA24BHcxPcA4HTgmsK+PzU4OPjcKsYvIiIiEmxkJDgreXxcAdcgv/oV7Nnj/9i8eXDxxe53pQ4+uPJ1pa40xxcREZHEC8tMnhp3zSplrvkHgd0b8OAUHP0PlW3XTsCWM2BirOKh1Uo1NbqfA3wXGAZePTAwUHqk7gTuHBwc/AZwBbAReMHAwMBPgZ8ODg7+CFd3bwVusiwiIiJSW2ElOLq74ZhjGj+mpMvn4cwzw5dZsMCVINm3r7J9bNtW2XpSd5rji4iISOJle11Nbr9gd0c3ZDXHn2MiD//7puDHzXyY3ht8XMuZnnCZ9Ec3d/pXTemSDwHzgBU+E+C/GhgY2Ae8B5cB8qHihwq/4za8EREREYkml4NMwHQnk3GPy2xDQ8FlS8A1qbz3XtfMM1thV/s//7my9aQRNMcXERGRZFuWAxMwxzcZWKo5/hw7hsCGzPHtXph3UPBxLWd6byIy6asJdC8HnhgYGLij3IIDAwN/BB4HTi66bxuuS/viKsYgIiIiEiybnQnIdne7+7q7Z+5XI8q5RkZgYiL48c5Olwm/fDn83d9Vto+nPrWy9aQRNMcXERGRZOvKwsmboTPrMpDB/e4s3K9GlHPlR1yJkUAdsN/x7vhVatERla9bI9UEuvcH5g8ODppyCw4ODmZw2R77lzy0m8q6uouIiIhEs3w5jI7C+vWwZo37PTrq7pe5enth0aLgx42ZyYT/4hcr28fznlfZetIImuOLiIhI8h2yHM4ahb71cPwa9/usUXe/zJXthY6QOT5TsPhVqT9+FdfoBkaBo4DXAD8os+yrgfm4mn4ADA4OepPiHVWMQURERKS8nh44X+WCI8nl4MILgx/vrGb6WHDffdVvQ+pFc3wRERFJh66epteETo1lOfjde8KXuecyOHZF5fsYa355wmoyun8AGODLg4ODfxu00ODg4AuBL+GyOoony88o/G7+URARERERJ5uFCy4IftxaV8e7Gg8/XN36Uk+a44uIiIi0mq4s7H9C+DL3XVHdPvY2f45fTUrOx4E3AIcDvxgcHPwF8HNcFojF1eU7GdeIJgPcX1jH4xV1/FkVYxARERGRWtu3L/ixPXvg1lvdv9//flcKJq5Km1hKI2iOLyIiIq1tIu+aM+ZHXEmPZTkXCG5180qrzQV5EvBEBds/MP46NVZxRvfAwMDDwMuAmwvbORn4KPAfwH/iOq6fXHjsFuCUwjqe7wNnAV+pdAwiIiIiUgePPBL+uJeR/dnPVrb90dHK1pO60xxfREREWtrOYdi0BLathNvWud+blrj7W92SV4c/fujLYfsGOOS5lW1/0ZLK1qshY211fWIGBwc7gRzweuC5wMGFh/4CXA9sBIYGBgbCWns2VV9fn926dWuzhyEiIiJJk8+7Mh0jI65JYy7XHtnIK1eGZ2qffDKccw7ccw/8/+zdeZxcVZn4/8/pTmfrtCJLBAIEJEEFF5SMa9TEneiMg1srfmfAwZ/L4IyAOoA609SMCzKDyLg76Igz4rSDooxG3CBq3AGj4EZHIYBBggtS6QTS6T6/P06VXV2pvWvt/rxfr3pV161z7z19u7rq1HOf+5yFC+Gtby3ftpRDDmlbsDuEcF2McU1bdjaH9PoY3/G9JEnax0Q2BbX3Zvd9bsFQmsxyYFn7+9UuP3gtjL2v9HOhH+iHsACmdjW2/ZUnwxM/0XD36lFujD/rQPdc4EBYkiTtY/Nm2LABpqZgfBwGB6GvDzZuhLW9PRt5VS96EVx+eW1tBwfT8anH/e8Pd99dd7caYaB7fnJ8L0mS9rH1kpTBPVli7No/CGsunruTW/7xJvjCg1u7jwMeB8/6Tmv3kVNujD+bySglSZLmpmw2Bbmz2ekg7vj49PKdOzvbv1bavr32IDfUH+QGeMhD6l9HkiRJmo3sWOkgN6Tl2a3t7U87ffvk1u/jfi0OpNfAQLckSVKx0dGUyV3K1FR6fq56wxtav49PtOeSRkmSJOlPhlanzO1S+gdhaFV7+9NOf/xJ6/dx/Pmt30cVC5qxkUwm83jgEcD+wECltiMjI//cjH1KkiS1zNhY+Uzl8XHYOoezPb7T4ssNBwbgjjvg6KNbux/NmmN8SZI0p6wchuvPKv1c6IMjhtvbn3aKZZJ4muVRF8KSg1u7jxrMKtCdyWSeQZp9fWUdqzkIliRJ3W316vK1pwcHYdUczvYYLJPl0iwTE6n8y/btsGwOT/bTwxzjS5KkOWlgCNZthE0bUuB3cjxlcoe+tHwuT0S55HDY9cvWbX/LP8ABj4HlnZ3LqOFAdyaTeQzweaazO24GtgN7m9AvSZKkzhkehrPKZHv09aXn56pTT4U3vrG1+5iYSOVfTpujk/30MMf4kiRpTpjIwrbRVJd7aHXK5h4YSoHYk7bDraOpJvfQqpTJPZeD3ADHvBq2tHCMHyfhmmfB8+/s6LGcTUb3P5IGwD8HXjwyMnJjc7okSZLUYUNDsHFjyjyemkqZ3YODKci9cePczkR+1avgzW+GPXtat497753b5V96m2N8SZLU23Zs3jdr+/qzUtb28rUpEHv0PEu4WP0q+PGbYaqFY/zJXekEQgeP7WwC3Y8HIvBXDoAlSVLPymZTdvHYWCpZMjycAt1r16byGqOjKSi7alV6bi4HuSH97l/7GjzlKeUn5GyGRYtat23NhmN8SZLUuyayKci9Nzu9bDJXjnDThpTNPdezt0sZGIIHnwE/u6C1+/n9FujgVDyzCXQvBXaNjIxc16zOSJIktdXmzftmbZ91VsraXrs2BbXnY3mNtWvhggvg7LNhcrK2dRYurC8L/OMfh/POa6h7ainH+JIkqXdtGy0/8WKc6njG8Zz3h+s7uvu+Way7bZbrS5IkdU42m4Lc2ez0pJPj49PLd+7sbP86afNm+Md/rD3IDfCKV9SX7X7HHfX3S+3gGF+SJPWu7Nh0BnexyfFUl1utc9/vO7r72QxiPw0szmQyT25WZyRJkirKZuGSS1Km8SWXpMeNGh0tX5pjaio9Px9ls3DiibB7d33rvf/9cMghqfRJCNXb33vv/D6Z0L0c40uSpPaayMLWS+CHZ6f7iVmM8YdWp5rcpfQPpskn56OJLNz0vtbvZ+dWmOjcGH82ge7zgV8B78tkMgc0qT+SJEmlbd4MK1bAGWekshpnnJEeb97c2PbGxqYzuYuNj8/fyRJHR2FiorF1x8bgk5+E446rfV/qNo7xJUlS++zYDFesgOvOSPWjrzsjPd7R4Bh/5TCEMuHO0AdHDDfa0962bRTi3sbXD/21tYt9qTxMh8ymRvejSbOyvw/4SSaT+TDwPaDiaZeRkZFvzGKfkiRpPiosM5KXD1Jv2JAmjax3ksjVq1NN7lLB7sHBNPnkfHTjjXDffY2vPzxc+/rz9WRCd3OML0mS2qMVE0cODMG6jWn9OJW21z+YgtzrNs7PiSgnsnDr5TA1izE+C4FarvjcA3f/dBb7mZ3ZBLo3kWZkz3tzDevEWe5TkiTNR7WUGal30sjh4TTxZCl9fen5+WbzZvjQh2a3jfFxWFDDcK+vb/6eTOhum3CML0mS2qFVE0cuX5uC5LeOpprcQ6tSJvd8DHLv2JyC/lN1TBpfSqyjrOGe381uX7Mw2wFpDQUYZ9VekiSpNWVGhoZg48aUET41lbYzOJgCsBs31p8h3uvyWfP33jv7be2t4bLI+XoyoTc4xpckSa3XyokjB5Y1FiSfS0plzLfDos5Vv2s40D0yMuJs7JIkqT1aVWZk7dpU9mR0NAXLV61Kwdf5FuSGylnz9TjySNixA3btqtzu8svn53Huco7xJUlS2+QnjiwV7J7PE0c2S6WM+XotWAZ7a5hksm8x3P/Y5uyzAQ5kJUlS9xseThnApcw2M3jZslT25B3vSPfzNfhaKWu+Hq99LfRXmazmwgvhec+b/b4kSZLUu5w4srUqZczXa79H1daub6CjfzcD3ZIkqfvly4wMDaUMbkj3+eXzNTjdTPms+dlYsgRe9ap9/1YhpNvTnw533FG+NrokSZLmj/zEkQuGUgY3pPsFQ/N34shmymfMN8ORw7DmfeWf75K/W4gxVm9Vg0wmM0SapX05aUKau4DrR0ZG2lwIpn5r1qyJ1157bae7IUmSqtm5s/VlRrLZtI+xsRT8HR5OQdu5LpuFFSvSfb0WLIDFi+GLX0zlYKA9f6sahBCuizGuafuO54heHeM7vpckqYdM7GztxJET2VTGIzuWgr8rh1OQfa6byMIVK2Zfo7t/KTz/zvQ32f0b2HIO3PMLWPYg2P8EuO+utk/4WW6MP+tAdyaTeTjwNuBE9s0QnwK+APzjyMjIDbPaUQs5EJYkSQBs3lx+csp8AHcuy//+e/bAfffVvt7ChbBtGxx8cOv61iAD3Y3p9TG+43tJkgTAjs1pQsY4lcp49A+msijrNsLyeTC+z//+U3tgqo7xPQALYMFiWPfFrjtW5cb4sypdkslkng98D3gO0E+acb3w1g/8OfD9TCZz0mz2JUmS1FLZbAryZrPTtarHx6eX76xh8pVel5+c88ILYWCg9vX27IF3v7tl3VJ7OcaXJElzwkQ2BXn3ZqdrVU+Op8ebNqRM8rlu+Vo4aTs8+kIIdYzvAdgLj//vrgtyV9JwoDuTyRwFfAJYDGwD/hZYDSzJLVudW3YLsAj4RG4dSZKk7jM6mjK5S5maSs/PB8uWwemnw9VXw9Klta930UXz42TAHOcYX5IkzRnbRlMmdylxKpVLmQ8GlsExp8PTrk51tOsJeH/rJT11QmDBLNZ9I2lw+x3gWSMjI8W/9S+BX2Yymf8Cvgw8Dng98NpZ7FOSJKk1xsamM7mLjY+nWtPzySMfCe98J7z5zXDPPdXbh5BOBpx2Wuv7plZyjC9JkuaG7Nh0JnexyfFUE3w+yWd333wpXFvj0C1OphMCR/fGGH82pUueTpqQ5tUlBsB/MjIyMg68mnSZ4zNnsT9JkqTWWb061eQuZXAwTag4X2zenCamPOec2oLckGp6z7eTAXOTY3xJkjQ3DK1ONblL6R9MEyjOOxH6FsF+x9fYfKKnTgjMJtB9GJCtZQKaXJt7cutIkiR1n+HhNPFkKX196fn5oFSt8losWjS/TgbMXY7xJUnS3LByOE08WUrogyPmyfg+b8dmuGIFXPs6uHtLbev0LeypEwKzCXRPADUVdclkMgFYmFtHkiSp+wwNwcaN6T6f2T04OL182bLO9q9dKtUqryTG+XMyYG5zjC9JkuaGgSFYtzHVpc5ndvcPpsfrNqba1fNF4cScU7vqW7eHTgjMJtC9FVicyWSeVUPbZ5Emr+mdXHdJkjT/rF0L27fDxRensh0XX5wer+2dmcZnJZuFyy+vL5M778wz58/JgLnNMb4kSZo78nWp11wMx56T7k/anpbPJ9tGYXJP/es95MyeOiEwm0D350g1+f4jk8k8tFyjTCZzLPBhUq2/z85if5IkSa23bFmaUPEd70j38yV4m6/Lfc01ja1/xhlN7Y46xjG+JEmaWwaWpckUj39Huu+hwG3T3H0jxPvqX+/BZzS9K620YBbrvhv4/0g1+X6YyWT+F/ga8GvSgPdw4GnAC0mXNN6eW0eSJEndpLAud6M+/Wk4/fTm9Umd8m4c40uSJM0te/7Q2Hq3fRqO6Z0xfsMZ3SMjI/cAzwZuIQ1yTwY+AlwFfAm4BHhp7rmbgRNHRkZm8e1JkiRJLdFoXe5CX/hCc/qijnKML0mSNAdN3tvYer/urTH+bEqXMDIy8hPgEcC5wBZginSpY8j9vAU4G3hkrq0kSZK6zdhYY3W5C+3d25y+qOMc40uSJM0RE1n4xXvhtssbW3+qt8b4IcbYtI1lMpkBYP/cw9+PjIxM5JbfH7gGiCMjIyc0bYdNsmbNmnjttdd2uhuSJEmdccklqcb2bILdJ58Mn/hE07rULCGE62KMazrdj17Wi2N8x/eSJGne27EZNm2AyfsgNjARJcDKk+GJvTPGn02N7n3kBr13lnhqAXA8qa6fJEmSusnwMJx11uy2cfDBzemLuo5jfEmSpB4zkU1B7r2zrDC3pLfG+LMqXSJJkqQ5YGgILr8cFjSYA7F4MRx7bHP7JEmSJKkx20YhznIOnr7FcP/eGuMb6JYkSZrvNm+GF74QFi5sbP2BgZQVLkmSJKnzsmMwOcs5ePoG4IjeGuM3tXSJJEmSekw2Cxs2pPtq+vpSULu/H3btgsHBtGzjRli2rPV9lSRJklTd0sPraBwgLIS+fpjcBf2DEPpg3UYY6K0xvoFuSZKk+Wx0FKZqvKxxagomJ9PPL3sZrF+fMrkNckuSJEk9KqZbnISVL4OD16dM7h4LcoOlSyRJkua3sTEYr+Oyxr174b774MorDXJLkiRJ3WjXbfW1j3tg6j749ZU9G+QGA92SJEnz2+rVqQRJvaamUja4JEmSpO4ytDqVIKlXnIJbe3eMb6BbkiRpPhserr10SaHxcdi6tfn9kSRJkjQ7K4dTne16TY5DtnfH+DXX6M5kMpOt7IgkSZLa7Kab4K/+Cnbvrn/dwUFYtar5fVJbOcaXJEmaY3Zthy3nwsL9YW8NE84X6h+Eod4d49czGWVoWS8KdxLC4cDHgYOBKeDDMcaLQwj/Cvw5sAf4JfDyGOPdJda/BcgCk8DeGOOadvRbkiTNcdlsKtUxNpbKfQwPw9BQp3vVuLPOgosuanz9vr50DNTrHONLkiTNFTe9H649vfH1Q1+q0d2j6gl0Z1rWi5n2Aq+PMV4fQhgCrgshfAX4CnBujHFvCOGdwLnA2WW2sT7G+Ns29VeSJM11mzfDhg2pxMf4eMpmPuss2LgR1q7tdO/qd9NNjQe5Fy+GgYH0uzsR5VzgGF+SJGku2LW98SB3/2AKcq/b2LMTUUIdge6RkZG2DIJjjHcAd+R+zoYQfgasiDF+uaDZd4EXtqM/kiRpnstmU5A7W3DZ3/h4ut+wAbZvnxnw7YXM71NPrX+dhQvhL/8SnvnM9DsZ5J4THONLkiTVYCIL20YhO5Ymelw5DANdNsa/7nWNrXfgE+Dov0mZ3D0c5Ib6MrrbLoRwJPAo4HtFT/0NUG4K0Ah8OYQQgQ/FGD9cZtuvBF4JcMQRRzSlv5IkaQ4aHS0/WePUVHr+tNPS417J/P7lL+tfZ88e+NSn4K67pn9fqQGtGuM7vpckSXWrJYC9YzNs2gBxKk3W2D8I15+Vsp+Xd9EY/86rG1vvt9+GJYfB0b0/xm9g+s32CCEsAz4NnBFjvKdg+ZtJlz5+osyqT4wxPho4ETg9hPDkUo1ijB+OMa6JMa456KCDmtx7SZI0Z4yNTWdwFxsfh625WckLM7/z7cfHp5fv3Nme/lazeXMKVjfqmmvgu99tXn80r7RyjO/4XpIk1WXHZrhiBVx3BvzsgnR/xYq0PG8im4Lce7MpyA3pfm9u+USXjPF3bIY9v298/ds+BX/4cfP60yFdGegOIQyQBsCfiDF+pmD5KcBzgZfFGGOpdWOM23P3O4ArgMe0vseSJGnOWr06ZWaXMjgIq3KzkteS+d1p27fDM54BpYdRtfuLv2hOfzSvOMaXJEldo9YA9rbRlMldSpyCW7tgjL9rO1z9jNlv55vPn/02OqzrAt0hhAB8BPhZjPFdBcufTZqY5i9ijLvKrDuYm9yGEMIg8Ezgxtb3WpIkzVnDw9BXZsjU15eeh9ozv9stm4VLLoGXvQxWroR77539Nv/wh9lvQ/OKY3xJktRVag1gZ8emA+HFJsch26Ex/kQWtl4C33oZfG4lTDVhjL/7N7PfRod1Y43uJwJ/BdwQQtiSW/Ym4N+BRcBX0jiZ78YYXx1COBS4JMa4AXggcEXu+QXAZTHGq9rcf0mSNJcMDaUa28W1t/v60vL8pIz5zO9Swe7CzO922rwZTjwR7rsPJiaat90HPKB529J84RhfkiR1j1oD2EOrU03uUm37B2GoA2P8HZvhmhNh8j6giWP8JQc3b1sd0nWB7hjjZiCUeGpjmfbbgQ25n38FPLJ1vZMkSfPS2rWp7MfoaMrMXrUqZXIvK5iVfHg4TTxZSmHmd7tks/DMZ8Lu3c3f9pVXNn+bmtMc40uSpK5SawB75XCaeLKU0AdHtHmMP5GFa54Jky0Y4z/pM9XbdLmuC3RLkiR1pWXL4LQKM5HXmvndLpde2pog9/r18LjHNX+7kiRJUrvUGsAeGIJ1G1Pd7jiVAuP9g6nNuo0w0OYx/o/+sTVB7gesgQc8ovnbbTMD3ZIkSc1SS+Z3u3zgA83ZzsKFKXD/gAekTG6D3JIkSep19QSwl6+Fk7anut3ZrSnb+4jh9ge5J7Jw08XN327/Unj6Nc3fbgcY6JYkSWqmapnf7ZDNwk9/2pxt/dmfpVrfkiRJ0lxSTwB7YBkc3eEx/rbR5m6vk5npLWKgW5Ikaa4ZrXEQvGhRmqiykhtvnH1/JEmSpG7UDQHsWmXHmrOdMAAPfX3nMtNbyEC3JEnSXDNWwyD4wAPhkEPghhsqt4uxOX2SJEmS1Lih1dXbLLg/7M0CU+XbxAjHv6Np3eomfZ3ugCRJkpps9eqUrV3JlVdWD3IDPPShzemTJEmSpMatHAYGKrd58BlUDHIDhNCkDnUfA92SJElzzfBwmkSynJNOguuuq21bn/hEc/okSZIkqXEDQ7Du8+WfDwOw+MDq21l0UPP61GUMdEuSJM0l2Wyq0f3nfw6LF88MeIcA//mf8JnPwOcrDJLzHvxgOPro1vVVkiRJUnUTWdh6Cdz5tVzWdmFmd4C+pfC0q2HXbdW39cB1reljF7BGtyRJ0lyxeTNs2ABTUzA+DkuXphp8L3sZrF+fMr2X1THZzKZNLeuqJEmSpBrs2AybNkCcgslx6B+E/kVwzFkpkaVwUsl7fg79S2Byd/ntPeKf29f3NjPQLUmS1Ih85vTYWKqJPTwMQ0Od2182m4Lc2ez0sl270v2VV8IHPzgzyP3c58KXvlR+fy9+MRx8cHN/B0mSJKmbTWRh2yhkx9LkjyuHU8mQTuxvIgu/uhR++HqY2jO9zuR4uh97P5y0PQW481YOw7V/V35/YQB2bIKhuXnVpoFuSZKkehVnTg8OwllnwcaNsHZtZ/Y3OpqeL2VqKj1/2mnTy045Bf7hH2B3iWyPJUvgIx9p/u8hSZIkdatSmdPXnwXrNsLyFozxK+0P0nOT90HcU3r9OAW3jsLRBWP8gSE4/Pmw7bIy60xAdmtzf48uYo1uSZKkehRmTo/nsinGx9PjE0+EnTvbt78NG+COO+CSS1LGdv75YuPjsLVoQDs0BF/+csryztfxXrgwPc4vlyRJkuaDiWwKLO/NTmdMT46nx5s2wESTx/iV9nfNibDpxPRzuSB3vn2poPUD16ea3aX0D6ZSJ3OUGd2SJEn1qJQ5vXMnvPWtcP757dnfxAQ86EHpfnKy/DYGB2FViQHt2rUpUD46mgLhq1bVX8dbkiRJ6nXbRlOGdCmlMqdbub+pCsHtQuWC1iuHU2Z4qc2HvlTPe44y0C1JklSPsbHymdMA//Zv8LrXwSGHtH5/995b2zb6+lIAu5Rly2aWNJEkSZLmm+zYdGZ1sclxuPsn7dtfpSzuQuWC1gNDqfxJcVmU0JeWD8zdpBYD3ZIkSfVYvTqV+NhTZgA6OZmyrL/ylebU6169OmVkVwquV7JoUarlbZa2JEmSVNrQ6hQMLhd8HvtAqn3drFrd1fZXTd/iykHr5WvTRJW3jqbyJkOrUlB8Dge5wRrdkiRJ9Rkehhgrt7n33lQ/uxn1uoeHU0Z2o5785JkB92w21fQ+++x0n83Ovo+SJElSL1s5nDKey5m6t7m1uqvtr5pVr5gZdJ/IwtZL4Idnp/uJbApqH30aHP+OdD/Hg9xgoFuSJKk+Q0Nw5pnV201OptrX9SoOREPKyB4aSpndkO4XLaptewsKLuDbvBlWrIAzzoALLkj3K1ak5ZIkSdJ8lS/30VdhjD01kTKkG1EciIa0vwVDKbMbcuVFBmrfXt6OzXDFCrjuDPjZBen+ihVp+Txj6RJJkqR6veUt8P73V87Y3rULrrmmvvrXmzenTPCpqVSqZHAQzjorBbq3b585aeSGDXDoodW3+ZznpPtsNq1TmMGdL4eyYUPavuVNJEmSNF8tXwtHvwLG3lf6+al74e6f1r/dHZv3rZd9/Vkp0F1cXuS+P8CWN1bf5sID0v1ENm17b8EYP18OZdOGtP15kMmdZ0a3JElSvYaG4ItfrJ5V/elP116+pDAQnQ9Aj49PL4cUNH/HO9L9IYdMZ3hXcsop6X50tHxd8XvvhUsvra2fkiRJ0lw1UaWs3z11BroLA9H5APTkeHq8KTfGLywv0r+4tu3ud2y63zaaMs1LmbwXbp5fY3wD3ZIkSY1YuxZuvnlmaZBi/f21ly8ZHU2Z3KVMTZXezhe/WHmbH/jAdJb2l78M991Xut3ERCrHYgkTSZIkzWcLH1D5+Tu/Vl+d7m2jKZO7lDi1bymUXbdV32ZYmiaWBPjjT1KmecntT8C1Z86rEiYGuiVJ0vzR7IkYDzkEXvzi8s+Pj6dSI7X4yU+mM7lr3c6TngTr15de50lPgle/Ov2czcJnPlN5/xMTzZtAU5IkSepF+z0MQqVKz3211+meyMKtl09nchebHE8lSwoNrZ6u2V1u/0/70nQ5kvHbq3WiuZNodjkD3ZIkaX5o1USM69eXLyEyOJjqaeeVC7R/9rPw7/9efh/F2yl09dXwne/AQQel7PKDDkqPv/GN6TZvfWuaHLOacpnjkiRJ0nywchgI5Z+fum/f4HQpOzbDZw6B33y5fJv+wVSXu3j/oUy4NiyEk36daokD3PR+uP3y6n0plTk+RzkZpSRJmvtaORHj8HCaMLKUvr70PJSfaPKv/xreV2bCm1LbKeVxj4MdO0o/l83ChRdW/z2gvgx0SZIkaa4ZGIKHnAk/u6D086WC08UmsvC1p6bSIZWEvukSJIX7X7dx38krQ19avuTg1G7Xdrj29Np+p1KZ43OUGd2SJGnua6T+da2GhmDjxnSfz+weHJxevmxZ5YkmqwW5Fy2a3k4jPvSh2rK58/0ulzkuSZIkzQcPewssKDP2LhWcLvbziysHucNCWJALaA+U2M/ytXDSdlhzMRx7Tro/aft0JjfAD/62+u+RV0twfo4wo1uSJM19Y2P117+ux9q1KSt8dDRta9WqlIGdD05XCrRXc9RRafuN+shHam9bLXNckiRJmusGhmDdF8tnVZcKThf6aZls8Lz+xakESaXtDCyDo08r//xvvlp5H4VqCc7PEQa6JUnS3Ld6dcpWLhXsblYW87JlcFqZwWilQHs1v/1t432C8iVNCg0OpiD3bDLHJUmSpFabyMK2UciOpYkbVw6nwHSz5bOqbx1NZT+GVqVgcbUg967tMFllwvu9u6pvp5qpvdXb1BOcnyMMdEuSpLmv1jrarVIp0F7NQQfNbt8LF1Z+fnAQLr54Zga6JEmS1G12bN43y/r6s1Igd/ksroAsp1pWdSlbzq2hUYXJLmuxYzPE+yq3WXA/OOFdtQXn5xBrdEuSpLmvljrarTQ8nALqjXjFK2a374MPrvz8Yx+bMtENckuSJKlbTWRTkHtvNgW5Id3vzS2f2NnZ/uVlf169Tf+ixrefPw7VrHxpCtLPoyA3GOiWJEnzRb6O9sUXwznnpPvt22dX/7pWpQLt1TKt8175ysb3m83Cz35Wuc3zn9/49iVJkqR22DaaMrlLiVOpxEg3GHpI9TZ9NX4PKKXSccgLA3DgnzW+jx5m6RJJkjR/VKqj3WrFE1Zu2QJXXVV5nSc9aXaZ1h/6EExUmPF94UI45ZTGty9JkiS1Q3ZsOpO72OR4qqPdDY5/B9zy8cptllS54rKSSschr3/xvJl8spiBbkmSpHYpDLRfcgl8/euwe3f59p/6VOP7ev/74Y1vrNzmRS+yZIkkSZK639DqVJO7VJC3fzBNFtkNBoZgv+Ph7i3l29Rb97vQrtsrP9+3eF5NPlnM0iWSJEmdMDwMCyrkHFx4YfX62uVs3w6nn165zdKlsH59Y9uXJEmS2mnlMIQyYczQ1x0ZzDs2wxUr4J6byrfpWwSrGixNuGs7bLusQoMBeN7NrZmYs0cY6JYkSeqEwrrdS5akZSGk4PdnPwtnndX4tt/whuptYkzBdkmSJKnbDQylTOUFQymDG9L9gqHuyGAunCxzate+z4f+1N+nfrXxvv6wyhg/THVPCZcOsXSJJElSpxTX7V61KgWfZ1tO5Dvfqd4mhNntQ5IkSWqn5WvhpO1p4sns1lSu5Ijhzge5ocokkQvgyJfBmvfOrq93fbvy83EyBdtP2t4dx6QDDHRLkiR1UismyBwcrN4mhBRg79TknJIkSVK9BpbNrsZ1q1ScJHIvLDlk9sHnqQqTzOfFqXQioBuPURsY6JYkSWqnbDYFmMfG4PDD07LbboPVq1M299DQ7Pdx6qnVJ6IcH09Z5JIkSZIaN5GF3b8hhVn37vt8/9LmTJa5bDXcu71ym8nxeV2+xEC3JElSu2zeDBs2wNRUCjQXGhxMdbk3bkwlTWbjVa+CN78Z9uwp32ZwMJVKkSRJktSYHZtTuZA4SckgN8DkLlhy+Oz3tfKF8NuvV27TP9icoHqPcjJKSZKkdshmU5A7m903yA1pWb7Nzp2z29fQEHzta7B0afk2fX1ORilJkiQ1qnACyskSE1AW2vxCmJjlGP9Bp6Ts8EpCX6pbPk8Z6JYkSWqH0dGUyV3N1FRqOxvZLPz85ymz+2lPg/7+FNiGFPweGkqZ47Od9FKSJEmarypOQFkkXzt7NgaGYP2XYMEyCAMzn+tbDAuGYN3GeTsRJVi6RJIkqT3Gxkpnchebbe3sSuVRBgbS8iuumH15FEmSJGk+qzgBZZFm1c5evhZOugO2fgh++RG49y5YdBCsOg1WvWpeB7nBQLckSVJ7rF6d6mJXC3YvXtx47ezC8iilTEyk2wtfCNu3m9EtSZIkNWpodaqJXWuwe+lhzdnvzR+HH75h+vGe36bH/UvgmL9tzj56lKVLJEmSWiGbhUsugbPPTvcbNkyXD6nk3nvhOc9pbJ/tLI8iSZIkzWcrh1NN7HbatR2uPb30c9eeDrt/097+dBkD3ZIkSc22eTOsWAFnnAEXXJDuH/IQOP/8VB970aLy6y5eDF/4QmP7bVd5FEmSJGk+msjC1kvgh2enGt1PujzVxu4frL7urttnv//v/k3l57ecM/t99DBLl0iSJDVTqfIh+eDzOefATTfBy18OV11Vev177208CH344bBwIezZU73tYU26dFKSJEmaD3Zshk0b0sSSk+MpuB36YO3lsPs2uPVy+M3VEEuMxfsHYajB8oQT2RRU/+NP4Ddfqtz2nl80to85wkC3JElSNptKeYyNpVraw8Mp87oRlcqHTE2lbO0XvAC++c3S2deDg43V6N68Gc49t7YgtyRJkjTX5QPE2bFUT3vlMAw0OMafyKYg996CZJZ8be7NL4STtsMRL4YrVsDeEuPx0AdHDNe/3+LgejX3e3D9+5hDLF0iSZLmt1JlRlasSMsbUal8SL5kyPBw+XrdfX3p+Xrks8h37qx9ndubcOmkJEmS1I12bE5B5+vOgJ9dkO6vWJGWN2LbaAo2lxKn4NbRFERft3FmKZP+wfR43UYYqHMi+MLgeq0TXh5/fn37mGPM6JYkSfNXpTIjGzbA9u2wrM4B6erVKSu7Urb20BBs3Jj2MTWV2g4OpiD3xo3177PWSSjz+voayxqXJEmSul2l7OtNG1L2db1B5+xY+WDz5Dhkc6UHl69N2791NC0bWpUyuevdH1QOrpey8ABYcnD9+5lDzOiWJEnzV7UyI6Oj9W+zUrZ2CLB7N5x9NmzZAv/0T/CkJ8Gznw3vfGcKrK9dW/8+a52EMq+RrHFJkiSpF9SSfV2vodXlJ5zsWwq7t6cJKn/xXvjVx+Cem2DZ0amcSSNBbqgcXC/lkGc1tp85xIxuSZI0f9VSZqRe5bK1p6bS7Zxz9t3n4CB861vwyEc2FuiulEVeyuWX1581LkmSJPWCWrOv67FyGK4/q/RzU7vg1k/vu8/+wbTOuo0p07te+eB6rcHuR19Y/z7mGDO6JUnS/JUPEJfS6KSQkILV27fDxRenwPY73wn9/bBrV+lg9Ph4Y3W284aHU7Z4LS68EJ73vPr3IUmSJPWCStnX/YOpnEi9StXf7ls6/XypYPTkeCqfsmkDTDQwxl9Zxxh/zfvmfdkSMNAtSZLms2ZPCllo2TI47TR4xztg0SKIsfo6jZZL+dGPYHJy3+UDA3DUUfCgB8HJJ8Mdd8BZZTJRJEmSpLlg5TCEcqUE+1LN7Ebk62+vuRiOPQdWvrB8QL1Qo+VS/vAjiCXG+P1L4JBnwwGPg6NOgZPugGP+tv7tz0GWLpEkSfNXsyeFLKfWGtrVyqVksykQPjaWstHzgfgNG1Lt72KLFsGPf2yZEkmSJM0f+ezrTRtSkHlyPAWkQ19a3mjNbEjrHn1a+vmHZ9dWVqRauZSJbKornh1L2egrc2P8TRtgssQYn35Y+7+z+z3mKAPdkiRpfsuXGRkdTUHmVatSALmZweHVq2Hp0lS6pJJK5VI2b943IH/WWfCa15SfUDPG9Huddtrs+i9JkiT1knz29a2jKcg8tCplcjczOFxrDe1K5VJ2bN43IH/9WbD6NeUn1CSm3+tox/jFui7QHUI4HPg4cDAwBXw4xnhxCGF/YBQ4ErgFeHGM8Q8l1n82cDHQD1wSYzy/TV2XJEm9Kl9mpJRSWdRDQ/Vtf8OGFJCuply5lHz97mx2elk+Q/zd74Y9e0pvr9EJNaUmc4wvSZLarjD7uhUqTVBZqFy5lIlc/e69BWP8fND8F++GqTJj/EYn1JwHurFG917g9THGhwKPA04PIRwLnAN8Lca4Gvha7vEMIYR+4H3AicCxwEtz60qSJNVv82ZYsQLOOAMuuCDdr1iRltezjVWrYO/eyu0WLixfLmV0tHzWdghp3VJmM6Gm1FyO8SVJ0twxkYVfXQpDD67cbsGy8uVSto1WyNoOEMqM8RudUHMe6LqM7hjjHcAduZ+zIYSfASuA5wHrcs0uBTYBZxet/hhga4zxVwAhhP/JrffTlndckiTNLZWyqDdsSOVOqpU3yWbhGc+Ae++t3G7hQnjXu1IZlVIq1fi+77406WQps51QU2oSx/iSJKnrlKqNPVDDlZs7NsM1z4LJKmUJw0I4/vxURqWU7Fj5sidT90EoM8afzYSac1w3ZnT/SQjhSOBRwPeAB+YGyPmB8vISq6wAbit4fHtumSRJUn0qZVFPTaXnq7n00upBbkiTRp5ySvnnV69O2dml5Gt1Dw1NtxkcnJ5o04ko1WUc40uSpI7bsRmuWAHXnQE/uyDdX7EiLa9kIgubTqwe5AaIe2DX7eWfz9f4LqV/EB5yFiwYmm7TP5gez3ZCzTmsawPdIYRlwKeBM2KM99S6Wollscz2XxlCuDaEcO1dd93VaDclSdJcVSmLutba15//fPU2AwPVA9LDwyk7u5S+PnjLW1KG+cUXwznnpPvt28tniEsd0soxvuN7SZJUk8La2PmM6snx9HjTBpjYWX7dbaMwNVHbfqqVGFk5nLKzSwl98LC3pAk111wMx56T7k/aXj5DXN1XugQghDBAGgB/Isb4mdziO0MIh8QY7wghHALsKLHq7cDhBY8PA7aX2keM8cPAhwHWrFlTMhguSZLmsXwWdalgd7NqX4cA73gH/Pzn8H//V36yy3x29oYNKZt8fDz1oa9vZpC83ISaUhdo9Rjf8b0kSapJpdrYcQpuHS0/iWV2LJUVqUW1EiMDuezsTRvSfifHU3A89M3M2m7lhJpzTNcFukMIAfgI8LMY47sKnroSOAU4P3f/uRKr/wBYHUI4Cvg18BLg5Nb2WJIkzUnDw6kkSCm11r5euxa+9KXyzy9YAOedBzFOB6/POisFr4uzsdeuTVnao6Mpm3zVqtQHS5OoBzjGlyRJXaNSbezJcchWuHJzaDWpQEa5SSQhTSS5AFa/hjKFJqYtX5uytG8dTfsdWpWC45YmaUjXBbqBJwJ/BdwQQtiSW/Ym0uD3UyGE04BbgRcBhBAOBS6JMW6IMe4NIbwW+BLQD3w0xviTdv8CkiRpDqg1i7qSq66q/PzUFOwsuDSy2mSXy5aZta1e5RhfkiR1h3xt7FLB7lrKjXz//6u+jzgBN70Pxj6QsrMrlRsZWGbWdpOEGL2qb82aNfHaa6/tdDckSVI32rmzsSzqbBbud7/G9jk4mOpsG9SetRDCdTHGNZ3uh9rL8b0kSSprIpsmntyb3fe5BUMpw7pcRvWu7fDZOufErrZN1a3cGL8bM7olSZK6R6NZ1KOjje+z1skuJUmSJNWn1trYpVz/hvr3V63ut5rGQLckSVIr3Hhj4+s2a7JLSZIkSftqtDb2b79V/76q1f1W0xjoliRJaoU//KHxdWud7FKSJElSYxqpjT25p/79VKv7rabp63QHJEmS5qT996+tXX9/yuCGdJ+fBLOWOuCSJEmS2mdhA3PwhL6ULa6WM6NbkiSpFY4+urZ2T31qyt6ud7JLSZIkSe0zkaWunOFa636raQx0S5IkNdvmzXDuubW1fd7zGpvsUpIkSVJ77NicJq+cuq+29vv/Gax+VW11v9U0BrolSZKaKZuFDRtg587a2p9ySmv7I0mSJKlxE9kU5N6brX2d1a+qv/63Zs0a3ZIkSc00OgpTU7W1Xb/eMiWSJElSN9s2CrHG8T1AWGRN7g4x0C1JktRMY2MwPl5b2wVeXCdJkiR1tewYTNY4vgd4ypWWK+kQA92SJEmSJEmSNGv9cNATOt2JectAtyRJUrNks/De99be/qCDWtcXSZIkSbMzkYWb3lfHCpNw86Ut644qM9AtSZLULKOjsGdP7e337m1dXyRJkiTNzrZRmJqob51ff6E1fVFVBrolSZKaZWysvuD15z4HO3e2rj+SJEmSGpcdg1hHIos6yhmQJEmSmmX1aujrg6kaZ2Xv709Z4KedVvs+stm0zthY2t/wMAwNNdZfSZIkSeUNrSblCdc4vgdY8Zz69zORTdnj2bG0z5XDMOAYv14GuiVJkppleBhe/era2+/aBVu31t5+82bYsCEF0sfHYXAQzjoLNm6EtWvr768kSZKk8lYOw7Wvhan7amvfvwSOOqW+fezYDJs2QJyCyXHoH4Trz4J1G2G5Y/x6WLpEkiSpWYaG4OlPr7394CCsWlVb22w2Bbmz2RTkhnSfX24JFEmSJKm5BobgyVfW1jYshfVfhoFltW9/IpuC3HuzKcgN6X5vbvmEY/x6GOiWJElqpgc9qPa2fX0pC7wWo6PlS6JMTaXnJUmSJDXXoc+EA59Uuc2Sw+GFd9afgb1tNGVylxKn4FbH+PWwdIkkSVIz5bOtKxkcTEHujRthWY0ZH2Nj5bc9Pl5fCRRJkiRJtdt9W+Xn+wbqy+TOy45NZ3IXmxyHrGP8epjRLUmS1Ez771/5+T/7M7j4Yti+vb662qtXpwB5KfWUQJEkSZJUn/4y4/C8BUsb2+7Q6vLb7h+EIcf49TDQLUmS1EzHHQeLF5d+bvFieNWr4LTTas/kzhseTlngpdRTAkWSJElSfR50apXnX97YdlcOQygzxg99cIRj/HoY6JYkSWqm4WEYGCj93MBA4wHpoaFU6mRoaDqze3Bwenm9gXNJkiRJtVn9KuhbVPq5vkWw6pWNbXdgCNZthAVD05nd/YPp8bqNjZVDmces0S1JktRM+cDzhg1pksjx8cZqcpeydm0qeTI6mmpyr1qVAucGuSVJkqTWGRiCp34VNp0Ik/dC3AthAfQvhnVfnF1AevlaOGl7mngyuzWVKzli2CB3Awx0S5IkNVsrA9LLlqXSJ5IkSZLaZ/laOOmO1gSkB5bB0Y7xZ8tAtyRJUisYkJYkSZLmFgPSXc0a3ZIkSZIkSZKknmagW5IkSZIkSZLU0wx0S5IkSZIkSZJ6moFuSZIkSZIkSVJPM9AtSZIkSZIkSeppBrolSZIkSZIkST3NQLckSZIkSZIkqacZ6JYkSZIkSZIk9TQD3ZIkSZIkSZKknrag0x2QJEmaN7JZGB2FsTFYvRqGh2FoqNO9kiRJktSoiSxsG4XsGAythpXDMOAYvxMMdEuSJLXD5s2wYQNMTcH4OAwOwllnwcaNsHZtp3snSZIkqV47NsOmDRCnYHIc+gfh+rNg3UZY7hi/3SxdIkmS1GrZbApyZ7MpyA3pPr98587O9k+SJElSfSayKci9N5uC3JDu9+aWTzjGbzcD3ZIkSa02OpoyuUuZmkrPS5IkSeod20ZTJncpcQpudYzfbga6JUmSWm1sbDqTu9j4OGzd2t7+SJIkSZqd7Nh0JnexyXHIOsZvN2t0S5Iktdrq1akmd6lg9+AgrFq173InrpQkSZK619DqVJO7VLC7fxCGSozx1VJmdEuSJLXa8DD0lRl29fWl5wtt3gwrVsAZZ8AFF6T7FSvSckmSJEmdt3IYQpkxfuiDI4ZLP6eWMdAtSZLUakNDsHFjuh8cTMsGB6eXL1s23daJKyVJkqTuNzAE6zbCgqGUwQ3pfkFu+cCyyuur6SxdIkmS1A5r18L27akcydatqVzJ8PDMIDfUNnHlaae1vr+SJEmSKlu+Fk7aniaezG5N5UqOGDbI3SEGuiVJktpl2bLqQWonrpQkSZJ6x8AyONpElG5goFuSJKmbNDJxpSRJkqTuNZGFbaOQHUuTWK4cTqVP1FQGuiVJkrrJ8DCcdVbp50pNXClJkiSpe+3YDJs2QJyCyfFUx/v6s1Id7+VrO927OcXJKCVJkrpJPRNXSpIkSepeE9kU5N6bTUFuSPd7c8snnGi+mczoliRJ6ja1TlwpSZIkqXttG02Z3KXEqTSJpfW9m8ZAtyRJUjeqZeJKSZIkSd0rOzadyV1schyyTjTfTAa6JUmSOiGbTRnbY2NpAsrh4VSeRJIkSVJvKp50cunhqSZ3qWB3/yAMOdF8MxnoliRJarfNm2HDBpiagvFxWLoU/u7v4PnPh/XrDXpLkiRJvaZ40sm+hRAnIcbS7UMfHOFE883kZJSSJEntlM2mIHc2m4LcALt2wb33wmWXwemnw6GHpmC4JEmSpO5XatLJqT0p0E1Rje7+QVgwBOs2woBz8DSTGd2SJEntNDqaMrnL2bMn3Z75TNixwwkoJUmSpG5XadLJQmEBPOqdcNQpBrlbwIxuSZKkdhobm87krmT3bvinf2p9fyRJkiTNTqVJJwvFvTB+m0HuFum6QHcI4aMhhB0hhBsLlo2GELbkbreEELaUWfeWEMINuXbXtq3TkiRJtVq9GgYHa2t78cWwc2dr+yO1gWN8SZI0pw2tTiVJavHzd8GEY/xW6LpAN/Ax4NmFC2KMwzHG42OMxwOfBj5TYf31ubZrWtdFSZKkBg0Pl5+QppTR0db1RWqfj+EYX5IkzVUrh4Fax/gBbnWM3wpdV6M7xviNEMKRpZ4LIQTgxcBT29opSZKkZqo10D01BVu3trYvUhs4xpckSXNfjWP8uAeyjvFboRszuit5EnBnjHGszPMR+HII4boQwivb2C9JkqTajI5CX41DsKVLYdWq1vZH6jzH+JIkqbdtG6X2MOsCGHKM3wpdl9FdxUuBT1Z4/okxxu0hhOXAV0IIP48xfqNUw9wg+ZUARxxxRPN7KkmSVEqtk1EC9PenUifS3NaUMb7je0mS1DG1TkYJ0NcPRzjGb4WeyegOISwAng+ULWITY9yeu98BXAE8pkLbD8cY18QY1xx00EHN7q4kSVLjFi2CoSHYuBGWOSO75q5mjvEd30uSpJ7w5CthwDF+K/RMoBt4OvDzGOPtpZ4MIQyGEIbyPwPPBG4s1VaSJKkjsll4//srtxkYgAsvhO3bYe3a9vRL6hzH+JIkqbdNZGGsyhifBdC3GNZ/CQ59Zlu6NR91XaA7hPBJ4DvAg0MIt4cQTss99RKKLmkMIRwaQtiYe/hAYHMI4UfA94EvxBivale/JUmSqhodrT4R5cKFsHixmdyaUxzjS5KkOWtblTH+AY+Fx34QXnAXHGKQu5W6rkZ3jPGlZZafWmLZdmBD7udfAY9saeckSZJmo5b63OPjsNVZ2DW3OMaXJElzVrX63A9cD0efVv55NU3XZXRLkiTNWatXw+Bg5TaDg7DKWdglSZKknjC0GvrLjPHDQlh6WHv7M48Z6JYkSWqX4WHoqzL86utL7SRJkiR1v5XDEMqM8eMe2HIO7Njc3j7NUwa6JUmS2mVoCDZuTPeLF898bmAg1eXeuNH63JIkSVKvGBiCdRuhv8wYfu9O2LQBJna2t1/zkIFuSZKkdlq7FrZvh9e9bmZ298QETE52rl+SJEmSGrN8LRz/DuhbWPr5OAW3jra3T/OQgW5JkqR2ixEuvBCmpmYu370b1q+HnWZ7SJIkST3lt9+BqT2ln5sch6wTzreagW5JkqR2u/hi2Lu39HN798J73tPe/kiSJElq3EQWtl1euc3A/drTl3nMQLckSVK7ve99lZ9/73vb0w9JkiRJs7dtFCiTzZ13++fa0pX5zEC3JElSO23eDL/5TeU22Wx7+iJJkiRp9u68pnqbnTe3vh/znIFuSZKkdslmYcOG6u1ibH1fJEmSJM3eRBZu+0z1dpO7Wt+Xec5AtyRJUruMju47AWUpixa1vi+SJEmSZm/bKIRaQqwLW96V+c5AtyRJUruMjcH4ePV2IbS+L5IkSZJm785raszWrlLDW7NmoFuSJKldVq+GBQuqt6uljSRJkqTOmsjCbZ+urW3wqs1WM9AtSZLULhs2wN691ds5GaUkSZLU/baNAv21te0zDNtqHmFJkqR22bixtmzt8XHYubP1/ZEkSZLUuOwYTNU4yaQTzrecgW5JkqR2GRurLaMb4NJLW9sXSZIkSbMztBr6B2tru+e3kP1la/szzxnoliRJ6kaf+1yneyBJkiSpkpXDQB2Z2t/565Z1RQa6JUmS2iObhfe8p/b2t93Wur5IkiRJao44WXvbnbe0rBsy0C1JktQel14Ku3fX3v7uu1vWFUmSJElN8KtLYeq+2tsvO7JlXZGBbkmSpPb4/Ofra794cWv6IUmSJKk5fl3nGP/xH29NPwQY6JYkSWqPyTouaQR4whNa0w9JkiRJzVFP2ZIF94Oho1vXFxnoliRJaov99quv/YUXtqQbkiRJkpok1jER5Yrntq4fAgx0S5IktcdvflN72wsvhIMPbl1fJEmSJM3ORBZ2fL329o82kaXVDHRLkiS12vvfD5s319b2+c+Hs85qbX8kSZIkzc73Xw3sra3tg8+EJSaytJqBbkmSpFbavh1OP722toODsGFDa/sjSZIkaXZ2bYdtl9Xe/peXwMTO1vVHgIFuSZKk1jr33Nrb9vXB8HDr+iJJkiRp9rbUMcYHiFNw62hr+qI/MdAtSZLUSj//eW3tli2DjRvTvSRJkqTudc9P6ms/OQ7Zra3pi/7EQLckSVIrPeQhne6BJEmSpGYKC+tr3z8IQ6ta0xf9iYFuSZKkVnrHO2prt3Nnqs+909p9kiRJUle7/7F1rhDgCEsUtpqBbkmSpFY69FBYsqS2tlNTMGrtPkmSJKmrTU3U1/6Y02HAEoWtZqBbkiSp1VaurK3d+DhstXafJEmS1NUW7V9f+xBa0w/NYKBbkiSp1U47rbZ2g4Owytp9kiRJUle7/3EQFtXW1vrcbWOgW5IkqdVe9SpYVMNAuK8Phq3dJ0mSJHW1lcPQX+OElKHP+txtYqBbkiSp1YaG4KtfTRnbCxbs+/zgYGqzcSMss3afJEmS1NUGhmDdRlgwBH1Lcwv7010+07t/MD2/bqP1udukxDctSZIkNd3atfCb36TJJrduhcMOS8tvvz2VKxkeNsgtSZIk9Yrla+Gk7XDrKGS3pvIkhz4Htn9h+vERwwa528hAtyRJUrssW1Z7vW5JkiRJ3W1gGRxdNL4vfqy2sXSJJEmSJEmSJKmnGeiWJEmSJEmSJPU0A92SJEmSJEmSpJ5moFuSJEmSJEmS1NMMdEuSJEmSJEmSepqBbkmSJEmSJElSTzPQLUmSJEmSJEnqaQa6JUmSJEmSJEk9zUC3JEmSJEmSJKmnGeiWJEmSJEmSJPU0A92SJEmSJEmSpJ5moFuSJEmSJEmS1NMMdEuSJEmSJEmSepqBbkmSJEmSJElSTzPQLUmSJEmSJEnqaQa6JUmSJEmSJEk9zUC3JEmSJEmSJKmnhRhjp/vQcSGEu4Btne5HFQcCv+10J+YRj3f7eKzbx2PdPh7r9vJ4t08vHuuVMcaDOt0JtVePjO+hN/+nepXHur083u3jsW4fj3X7eKzbqxePd8kxvoHuHhFCuDbGuKbT/ZgvPN7t47FuH491+3is28vj3T4ea6m5/J9qH491e3m828dj3T4e6/bxWLfXXDreli6RJEmSJEmSJPU0A92SJEmSJEmSpJ5moLt3fLjTHZhnPN7t47FuH491+3is28vj3T4ea6m5/J9qH491e3m828dj3T4e6/bxWLfXnDne1uiWJEmSJEmSJPU0M7olSZIkSZIkST3NQHcPCCE8O4TwixDC1hDCOZ3uz1wSQvhoCGFHCOHGgmX7hxC+EkIYy90/oJN9nCtCCIeHEK4JIfwshPCTEMLrcss93k0WQlgcQvh+COFHuWOdyS33WLdICKE/hPDDEMLnc4891i0SQrglhHBDCGFLCOHa3DKPdwuEEPYLIVweQvh57r378R5rqXkc47eOY/z2cYzfPo7x288xfvs4xm+fuT7GN9Dd5UII/cD7gBOBY4GXhhCO7Wyv5pSPAc8uWnYO8LUY42rga7nHmr29wOtjjA8FHgecnnste7yb7z7gqTHGRwLHA88OITwOj3UrvQ74WcFjj3VrrY8xHh9jXJN77PFujYuBq2KMDwEeSXqNe6ylJnCM33IfwzF+uzjGbx/H+O3nGL+9HOO3x5we4xvo7n6PAbbGGH8VY9wD/A/wvA73ac6IMX4D+H3R4ucBl+Z+vhT4y3b2aa6KMd4RY7w+93OW9Ga6Ao9308VkZ+7hQO4W8Vi3RAjhMOA5wCUFiz3W7eXxbrIQwv2AJwMfAYgx7okx3o3HWmoWx/gt5Bi/fRzjt49j/PZyjN8VPN5NNh/G+Aa6u98K4LaCx7fnlql1HhhjvAPSwA1Y3uH+zDkhhCOBRwHfw+PdErnL7LYAO4CvxBg91q3zbuAfgKmCZR7r1onAl0MI14UQXplb5vFuvgcBdwH/mbtk95IQwiAea6lZHOO3n+9fLeYYv/Uc47fVu3GM306O8dtjzo/xDXR3v1BiWWx7L6QmCSEsAz4NnBFjvKfT/ZmrYoyTMcbjgcOAx4QQHtbhLs1JIYTnAjtijNd1ui/zyBNjjI8mXe5/egjhyZ3u0By1AHg08IEY46OAcXr4EkapCznG15ziGL89HOO3h2P8jnCM3x5zfoxvoLv73Q4cXvD4MGB7h/oyX9wZQjgEIHe/o8P9mTNCCAOkAfAnYoyfyS32eLdQ7jKkTaQ6lR7r5nsi8BchhFtIl50/NYTw33isWybGuD13vwO4gnT5v8e7+W4Hbs9ligFcThoUe6yl5nCM336+f7WIY/z2c4zfco7x28wxftvM+TG+ge7u9wNgdQjhqBDCQuAlwJUd7tNcdyVwSu7nU4DPdbAvc0YIIZDqQP0sxviugqc83k0WQjgohLBf7uclwNOBn+OxbroY47kxxsNijEeS3p+vjjH+PzzWLRFCGAwhDOV/Bp4J3IjHu+lijL8BbgshPDi36GnAT/FYS83iGL/9fP9qAcf47eMYv30c47eXY/z2mQ9j/BCjV8h1uxDCBlJ9qH7gozHGt3W2R3NHCOGTwDrgQOBOYAT4LPAp4AjgVuBFMcbiyWxUpxDCWuCbwA1M1zl7E6mGn8e7iUIIjyBNINFPOqH5qRjjP4cQDsBj3TIhhHXAG2KMz/VYt0YI4UGkDA9Il91dFmN8m8e7NUIIx5MmYFoI/Ap4Obn3FDzW0qw5xm8dx/jt4xi/fRzjd4Zj/NZzjN9ec32Mb6BbkiRJkiRJktTTLF0iSZIkSZIkSeppBrolSZIkSZIkST3NQLckSZIkSZIkqacZ6JYkSZIkSZIk9TQD3ZIkSZIkSZKknrag0x2QJM1eJpP5GHAKcOnIyMipne1NeZlMJuZ+XD8yMrKpk32RJEmSupljfEmqj4FuSapDJpM5DxgBGBkZCW3Y3zpgHXDLyMjIxxrcxqnAkcAmB56SJEnSTI7xJWluMNAtSd1tHWnQ/XXgYxXa3QH8Indf7FTgKbmfNzWtZ5IkSZIasQ7H+JLUdAa6JWkOGBkZORc4t9P9kCRJktQcjvElqT5ORilJkiRJkiRJ6mlmdEtSi2UymfsDJwJ/DjwCWAEsBe4EvgX8+8jIyHeL1jkSuLlg0VMKJnnJe3m+pl+piWpydfv+s6D9SCaTGSnaxlEjIyO3FO3vqJGRkVvK/C63ACsL9130/AOANwHPz/2efwA2A+ePjIxcV2qbJbbxl6RLMR8DHAiMAzcAlwEfGRkZmahlO5IkSVKrOMZ3jC+p+5jRLUmtdybwSeBk4GHAQG75EcBLgW9nMpm/L1pnkjRIHs89nsg9LrztrrLf3bl2+UHjeIltTDb0G5WQG0j/EHgD8CAgkAb7LyT9jn9RZf1lmUzm/4ArgOcBh+R+h/sDTwI+AHw9N9CWJEmSOskxvmN8SV3GjG5Jar3fABcBo8AvRkZG7s5kMoE0S/rrgL8H3pXJZL45MjLyQ4CRkZHbgIMLZoD/9sjIyLp6djoyMjIKjGYymU2kiWr+bWRk5Lxm/ELFMplMP/C/pEyQPwCvAq4YGRnZm8lkjgU+CFxaZTP/BTwX2Ar8E/D5kZGRbCaTWQw8k3QMHw98FDipFb+HJEmSVCPH+I7xJXUZA92S1GIjIyMfLLEski4jPCOTySwATs/dXtHm7jXLC4A1uZ9fNDIy8rX8EyMjIz/NZDLPBn4M7Fdq5Uwm8xzgL0lfGNaNjIz8umD9e4ErM5nM9cDPgb/MZDLHj4yMbGnB7yFJkiRV5RjfMb6k7mPpEknqvC/k7td2tBez85Lc/bcKB8B5IyMju4ALKqyfH/z/V+EAuGgbtwPX5B4+q9GOSpIkSW3gGN8xvqQ2M6Nbktogk8k8CPhbYD1wNDDEvicbD2t3v5oon+lxdYU2lZ7LfwF4ZSaT+esK7e6fu19Za8ckSZKkVnCMX/U5x/iS2spAtyS1WCaTOYk0Uc2igsX3APcCEVgIPAAYbH/vmmZ57r5kpkbO7aUWZjKZAdLM65AGufcv1a7I0tq7JkmSJDWXY/w/cYwvqWsY6JakFspkMgcAHyMNgK8G/hn4/sjIyO6CNk8DvtqRDjZfbOC5/oKfX5KbYEeSJEnqSo7xa3rOMb6ktrNGtyS11gbgfqRZyv98ZGTk64UD4JyD29+tfewt+HlxhXblMjF25O4rXZpZ8rncRDR/zD18eIX1JUmSpG7gGH+aY3xJXcNAtyS11uG5+1/kJmsp5ekV1p/K3YdZ9KGWbfyh4OfDSzXIZDLHUGZGdeDa3P36Cvt4aoXnvpW7f1Emk/GzSZIkSd3MMf40x/iSuoZvNJLUWvkshmMymcw+WRSZTOZ44OQK69+Tu99vFn2ouo2RkZFx4Je5hy8o0+zNFfaRvxRxbSaTWVf8ZCaTWQK8scL6H87dH1OlHZlMZjCTySys1EaSJElqIcf4OMaX1H2s0S1JDcpkMgdWabIX+DIp22J/4BOZTObvR0ZGfp0bxP0l8F4gCxxQZhs35u6Py2QyTxgZGfl2A129EXgesCGTyVwwMjJSbjKZTwJvAf4mk8n8GPjPkZGR3ZlM5nBgBBgGdlF6kphPA9cDjwY+nclkXgl8dmRkZDKTyTwU+ADTk9nsY2Rk5HOZTOYK4CTg/EwmcyRw0cjIyE0AueP1KOD5wGnA8ZSZ+EaSJElqlGP8GRzjS+opBrolqXF3VXn+RyMjI8dnMpl/Bc4mDeCen8lk/kgaSA4AN5MGnp8os41NwC+ABwPfymQyf2A6e+MNIyMjl9fQz0uB1wOrgFszmcxdpNngAdaOjIzkB5PvzPXxWOB9wHsymcw9pCyRCeCvgfOBlcU7GBkZ2ZvJZF6U6+/hwOXAfZlM5l5Szb89wIuAz1Xo5/8DPgK8BHg18OpMJjOeW/f+zLwKqdKEOJIkSVKjHOPnOMaX1GssXSJJLTYyMnIOaQD5fWA3afC7FXg7KYNhe4V19wJPAy4BbgEGSYPQlcCyGvc/RqqrdyVp4H5AwTYWFLTbCawF3kUanO8lDX4/DTx+ZGTkf6rs51ekLIz8+oE02L4ceMLIyMiVVdbfNTIy8tJcX/8L+BXpc2oZaSKcq4F/AFZXyFiRJEmSWs4xvmN8Sd0nxOgJM0mSJEmSJElS7zKjW5IkSZIkSZLU0wx0S5IkSZIkSZJ6moFuSZIkSZIkSVJPM9AtSZIkSZIkSeppBrolSZIkSZIkST3NQLckSZIkSZIkqacZ6JYkSZIkSZIk9TQD3ZIkSZIkSZKknmagW5IkSZIkSZLU0wx0S5IkSZIkSZJ6moFuSZIkSZIkSVJPM9AtSZIkSZIkSeppBrolSZIkSZIkST3NQLckSZIkSZIkqacZ6JYkSZIkSZIk9TQD3ZIkSZIkSZKknmagW5IkSZIkSZLU0wx0S5IkSZIkSZJ6moFuqQ4hhCNDCLHgdl6n+6TeFkI4teg1ta4ZbaVuN9dez0W/y8c63R9J6nW+r6oeIYRNBa+XWzrdH6kdQgi3FLzuN3W6P7MRQjiv6H3/yE73Sb3JQPccUCL4Wu62M4Tw6xDCt0MI7wkhPCeE0N/p/mt+CyE8vOh1en2d6z+yxGv9n+rcxr94AqN+IYR1RcdtU6f7JEmSWqfE945bGtjGv5YYu729Bd2Vul5RgL7nT/xLUqcZ6J5fBoFDgccDrwU+D/wqhPDcjvZK892NwO8LHj8yhHD/OtZ/co3L6tnG1+tcX12iRPD91E73Sb3BTDBJaq2QvAd4Q9FTIzHGN3WiT+oeZnN2D8dEaoTfw9QtDHTrCOD/Qghnd7ojmp9ijBH4ZsGiPuCJdWyiVFD78SGEgVpWDiEsAh5TsGgP8N069i9JkqQKQgh9wIdJyTaF/iHG+M8d6JIkSZqDDHTPTb8Gjipxezjw58B7gfGidd4RQjixnZ2UCnyj6HE9GdlrSyxbCpxQ4/p/BiwuePyDGOPuOvYvSZKkMnKlEj8GvKJgcQT+Psb4rx3plCRJmpMWdLoDaom9McZbyjx3I/D5EMK7gS8DD8otD8D5IYSrchm2Ujs1FOgOIRwDHJx7OEXKDH9KwTZqycwu3ldxXySpqhhj6HQfJKnbhBAWAJ8AXlyweAp4dYzxPyqt6/uq6hFjXNfpPkhqXIzxPOC8DndDc4AZ3fNUjPGXwEuKFj8CeFgHuiP9ELin4PGaEMLSGtYrDFLfCPxfweMn1bhv63NLkiQ1WQhhIXA5M4Pck8Cp1YLckiRJjTDQPY/FGH8A/KBo8VNKtZVaKcY4CXy7YNEA8LgaVi0MZn8D2FzweG2uHmRZuUtpH1+wqLgfkiRJqlMIYTHwWeB5BYsngJfGGP+rI52SJElznqVL9ANSjeK8w+tZOYRwECkLfDWwH9AP/AHYDnwnxnhXc7r5p/31kQKTq4BDgJ3ALcDXY4zZWW57NWlSwkOBe4Hbge/HGH89m+2W2M/hpCDuA4Eh4Pek4/XNGOPdTd7XcaTa7IeRytOMAdfEGP9YZb1VpON8KCn4exvwtRjjb5vZvyJfB55d8PjJwNVV1inMxv4mcD2wi1Sjez/S7/6jCusfD9yv4PH1lV5HIYQAPBh4KOmYDgH3kf6GN5Hqe++p0meVEUJ4EPBo0vtQP3An8K0Y46862jEghLA/aZLUQ4ADSO89vwG+3YL3iD9j+j1uIXBjjPHzday/gvRedhip/vx24Hsxxq1N6Nv9SCeYVpCOQ5b0d/pejPHW2W6/jn4Mkj57HgwcCCwB/gjcBVybu2qpp7Tz2IYQHkCa3+Bw0nvgb0nvlddavkzSbOWuyrsSeFrB4vuAF8cYr+xQn9YADyG9x+4mzWm0Kcb4uyZsuy1jhNz3oCcAR+f2tRu4AfhGjHFvhfUCsIY0xsr3bytwdYzx3gb7soA0Hn5ori/LSGPw35OustySS2TpKSGE/UjH+BDS+ALS7/Rz0u80q++brRZCGCB9PzoSWA7cTfpbf7PRv3Wz5F6Hjyb9Hx4ELCKN27aS4gYTTdxX4f/kgaQrhz8TY9xe4/r9pNfBg3LbuBu4mfSecV8T+vdI0jh2OWmsvyO3/W+387tk7rvXscBK0nhwL+n1fjPw3Rjjrnb1pVnaeWxb+bmiWYoxeuvxG+mDLBbcbqlj3bcVrfvhGtZZA1wI/KRo3VK37wEnzeJ3OS+3PABnAtvK7GcP8D5g/waO3xNz/Sy13b3AF4ATKvWvxv28GNhS4VhNAF8BHlfHNjeV+rsDzyedxCi1n3HgX4AFJbb3BOBbFY7FfwD7teh1/ISi/V1dpf1hRe0PzS2/umDZ31XZxplF2/i3Em2W5P52nyIFgyq93ncBHwVW1/F7n1q0jXXNaFuwTl/uf6NwvTHg6Cb8zdYVbXdTg6/XxwJfI9XsLHVcvws8vsq2b6nytyl1q9jf3HafQ7pSYLLCdq4DnlvHcStc92MFf6c3AL8qsf0tReufV/T8kbnlJ5Der/ZWOI5PaPBvvQb4Ium9ttxx+DFwMhCa/drPtV8JnJ37e1TqR8y9Hs4AltTZh1pv+/S11N+1g8e23GvkEOC/SIPxUvu5Dfjr2b43ePPmbe7eqPK9g5QE8I2iNruAZzWwr5rfV8u1Bf4K+GmZ97xJ4JPA4Q0ei3aNEQaAfwBuLbOPXwPDZbZ3CinIU2q9e4DXA3019u3+wMtJpQLvqfI5+UfgInLj8zpeT7XeziuxrU3lXpdV+hCAk0jfgcqNoSLpc/qa3DFd2MT/qU1F+1lXoe26oran5pYvJH2/21Gm7ztzz5cdF9HEMVHRdg/MvRburPJ6eQ9wYI3H7Lyi9Y/MLX8o8DlKj6n+sob/tYXAm0kJb6X6eTfwzkrHsUKflwBvIo21yh2HLPCfwGF1bPeWgvU3VWk7QHrfupT0vlHp77oH+DTw6Dr7UOttn76W+7t28Nju8xrJLW/Z54q35tw63gFvTfgjzi7Q/e9F6/57lfZPbeBNLOb+2at+IJT4Xc4jDaquqnE/P6XKgKpof2+ifHCt+I3+ZaX6V8M+7gd8tc7j9W5qGHRSYkAH/FuN+/g80F+wrddSeXCXv11PC4LdpA/e8YL97KLCIJIU9Mm33Vqw/J8Llv9vlX1+puh3+/MSbT7ewOt9F+nS3Fp+71OL1l3XjLa59ktIlw0XrvN94KAm/c3WFW17UwOv17+jetAy/z/44grbvqWBv1PZ/gIPAL5U5/Y+Wek1W7DtwnU+ltvXNRW2u6Vo/fOKnj+S9P5Uy3GcBM6t428cgAuo7X3yT8cVeECTX/v9dfbhT8cOWFlHH2q97dPX4r9rh49tqdfIU6j8JbPw9u5mvEd48+Zt7t2o8L2DdDXdd4ue3wmsb3BfNb+vFrclBaxqHcNtB46to1/tHCMso/IYofB2TsF2FgD/XeN6H6GGE6nsewKjltvvgKfV8Xqq9XZeiW1tKvW6rPI7HUoqW1jv/o9v4v/UpqJtr6vQdl1R21NJyT/lEpyKb98Ehsps+9QG/xaV+vtSUhC71m39HnhyDcfsvKL1jiSNhccrbPsvq/yv7VfHa+EX1BHIJGVN31zHcdgFvKzGbd9SsN6mKm3/uY4+5G97gdfX0Ydab/v0tdTftcPHtvg10rLPFW/NvVm6RMcXPb6lSvvimscTwM9IZ8/+SPrnPzS33cLJBF9C+lJfPAFmNf3AKPCs3OPdpOzr35CCeGtIl4rkPZR0hvIZ1TYcQjiDlNFeaDK3/dtIGSmPImXADZDe3E6rp/MhhCFSSY7ji576Ayng+Ifc9h9Hunwr73Wks9//r879vZGUmQHpd/kBKQt+EdNlWfKeA7wFyIQQ/op0Fh3SG/OPgF/mfj6eVEYh71GkQPyp9fStmhjjRAjhu6STKTD99y1XM7u4bEmpn6tNSLm2sAvMrPGdV/ya/z3paobfkj4oh0jlE44hvcYh9f0TIYS7Y4xfrNKHlgghHEjKuCmsdf4FUrC4Ky5DCyG8jHSyLe9G0uWL95Euyz2B6WM6AHwshPDDGONYi/t1KOnk1EOLnrqLNHHqb0lfPB9JyjLOewlw/xDCc2OMU7XujvRFdF3u8V7S/+1tpLIjq2vYxlOAS5guR/ZrUoB3Z65/j2H6ddwHvD2EsDvG+O4atv0f7Pu+t4cUyNhO+lKwhunLe/P9+UYI4ckxxj/UsI9aBKZfC5D+X28mXZ1wd+7xgaT3q8K+PBL4agjh0bH7Ljdu57F9KPA/TJdqup30GrmH9Bn0eNLrLe91IYRrY4z/Xcc+JM1jIYQDSFcmPqpg8T3AiTHGTsx/8j5S1h2k7ys/IL33DZBK2xWObQ8B/jeE8KhY5dL2DowR/ofpMcJO0mfEXcD+pKshhwravz2E8N0Y4ybgg6TAH6TPlu+RxgeDpPf8ws+WvyGNnz9WpT/FY+I7SUlGvyeVfdyPFHQ6qqDN/sAXQgiPjTFWKifYdiGEh5L+locWPXUfKRP/DtK47EDgEaRyG91mkJS89Mjc43tI3zHvIr02HsfMv/VaUnb1K1rdsRDCmaSrwAvHb5Okq9RuIR3nFaQrOxfmnn8A8OUQwrNzr+NaPZH0+i0cC/+IdDweyMxSrSW7S4o55OduKo4JPBo4uKD9McDVIYQnxCqlWnOlNK4m/S8Uupn03edeUomURzPzu+R/hRAGY4wfrtL3ehT/D2dzfdhBen9ZSnpvPJYUhyF3/28hhPEY4web2JdZ68Cxbcnnilqg05F2b7O/0WBGNyk4V3yp3Zoq6zydFFT4d1JAcqBMu6XAK9m31MOL6vxd8uvvJl3Wv6SofSBdQndf0XonVtnPw9k3+/Ey4OCidn2kshX5y8B+X7TOeVX2859F7e8BXlV83EhZ6xeyb2bfq6psf1NB250Fv9PHgAeWOFZ/V/Q330UKJubPfv8fJUpa5I7BvQXrTQHHteC1PFL0+59ToW3h5UJ/U7B8GTMz048ps/6xRfvaUqbdf5MGZGdRoSQJaVB/SdE2dwCDVX7nU4vWWTfbtqQP9JuK2v4HBRn8Tfp7rSvax6Y6X6/58glXAKtKtH8oaZBauI/RMts+jPT+8ZKi9m/ILS91O7jEdvpJJ6cKt3Et8PQKx+BnRe3PrnIcCttmC/6n3kWJ8ksUZTOwb7bDH3L3dwIvoOhqENKXiE8XrbMHeFiVfp5StM4U6STXfkXtFpACtsUZO59s4mt/AWlA+b+k96P7l2nXRzoxekPRtt9Xpv2ygtdDYRbi7RVeN0cCi6v8XT/W4WNb/BrJf5b+mBKZlaQvCKNF6/yaJr9nePPmrfdvlPjeQaqF+uOi5b+nyveKGvZVz/tqqfe8SeDtxe+tufbPYfrzM397TZV9tHuMkO/ffaTSJYuL2i4jjfkL1/ku6XMy/9nyToo+M0lBmfOL1tte7T2fFAz/FvAaKlz+T6qP+9mi7d9Aiazx3Ofckbnbu4vWWUv5z+FSf9NNha/LKr/LshJ/mzuBVwNLy6zz8Nzr6bd0T0Z3/rX+O9IJiwVF7RcA57Dvd8x9Mk1p7pjomUX73EMqnbJPaRLS9+B3FrW/nQolSdl3nJMfT/8CeEaZ3+3AomWl/tci5WMCL2Xf2Ea18dhi9i1xsZUSVzmQvr99sajtvcAjquzjloL2m6q0fSupTOJbSCdvSl7JQQrqn08ae+e3vRs4okz7Zn0PK/67HtnhY9vyzxVvrbl1vAPemvBHbCDQTfpSe13Rej+scb1ldfTtmKIPhO/V+bvk34TWVlnvlUXrfKpK+2uK2pcMghS0P459g9yRCoFu0uCssO0u4IlV9vPaonV2UuEydfYdGEXg/Cr7eGdR+/yb8cepUC6lRN/e2YLX8rqifWws0+5AZg6GVhc9f23Bc68os41XFe2rZNkeynygV/gdzqrnw40mB7pJGaDF5QlGmv23KvP32lSlfanX63upcLksKQujcMBwX5X/ieI+nVrn7/TGovUvp8qlxqQMpsLA6jhwQIX2xccgUuWkVtH655VY/7fAQ6qs959F61xToe0y9h2onVll+49l30tG9/my0eBrP9Tzv0jKcPpOwbZ3UWUOBxqs7Vnm7/qxDh/bUq+R71DmcuXcOn2kq1oK19lQ73Hw5s3b3L5ROimlOGC4A3hkE/ZV0/tqibaRNE4sW/Ist84zi9b5fpX2nRgjTFZ6L859Pn6naJ38Z0zJMXDBupcXrVctUWhlnX+/4hKZ1bZf/Nl1ZJ37q/lznDT+LNzXz6lxnEEaY9T8fbjOflcbD60r8Rr5HdXHgG8vWueCZh3LEusuY+Z3kXGqfJfPrXdaUR/Lftcs8VqJpAzesv9bJbZRvH6kekzgkeybfPCUCu3fUtR2DFheoX0faV6ownW+VaVPtxS03VSl7WHUWJM/1/4Ftf5Nyrw+T63ztVPze0Cbjm3LP1e8teZWfOmC5rAQwmAI4bgQwutJWRePLnh6ghTIrCjG+PsY485a9xljvAnIFCx6TAjhyFrXzzkvxliqpEShS0jZZ3lPKdcwhHAs05cAQsp8PbPSxmOMPyFlUtTjdUWPMzHGb1XZz3tJl5/lDVJfuZQtpAk0Knk36U06bz/SsXtNrHwp5YdJGel56+roV62+Szrjn/fE3KzXxZ7E9OVHv4n7lrIofL08mdKKl3+9VKMY461l1i8pxvguUh3zvOF61p+NEMIG0sB0eW7RXtKXnEzZlTrrRlKAL5ZrEGO8E/hAwaKFzCzH0jQhhEWkExV5vwT+X6xyuVmM8W7S5cH53yN/RUut/i/G+KE62pfy+hjjz6u0OZ00kVXeutz7YSmnkN4b8q6KMV5UaeMxxu8B/1S0uPh9sCExqfl/McY4zsy/wRLgL5rRlyboxLG9jzRvQNnyLbn3/38uWlz2s1SScg4AHlLw+A5S4KfTZSo+HGP8VKUGMcYvk4LEeSeEEAZLte3gGOF9McaNFbYfSWP7QvsBn40xXlJl2/9W9HhdpcYxxm1VtlfsjaTXQ17bxsSVhBAeyMzvV7uBk2odZ8QYx+v5PtwGp9cwBnwnKXksr5Wf76cx/V0E4HU1fJcnxvgR0vxJea8MISwt1754deCUGOPvau/mPmqJCfyIfb9rn16qbQhhAenqh8I+/nWMcUeF7U+Rjt/2gsVPCCGsqdSvWsUYb6/yfb+4/aeZ+Tfplv/hTh3bpn6uqHUMdM9NK0MIsfhGyg6+kTSoKaxrvQt4SbUg7Cx8rujxY+tYd5xUC6mi3BvXVQWLlocQDi7TvLju9QXVBqk5HyVdRlVV7s3sLwsW/YF9B6HlvKXocT11ut8dY5ys1CDGeAcpa6HQh3KBoUrr7SFdrpj38BBCKNe+ETHGe0m1rvLux3TNuULl6nPn1RLoLq7f/Y2qHaxd4Wt+TZlgfVOFEF6R22/+g3QceF5u0NitLooxTtTQrvgLXqnXRDM8n5n1996We01WFWP8MekkQ95z6thv8RfNet1KuiKjophqs19YtPhlpdqWWD5SY1/+nVQXMm9DCKG4bl5bxBhvINXoy6vns6eVOnFsPxVjvKWGdleTguJ5rfpfkzR39TEzoaJTLqixXeEYo49UnqKUTo0R3lVDm6sbXO97pPFiXlPf82OM9zHz+1m3fA7/FTPnpfhgjPFnnerMLN1CylKtKMb4R2Z+j3tEs7/HFfjbgp9vJn2HrtXFBT/vR6pDX4tNMcbr6thPKbXGBD5Iumol73llAplPZWb9XmqOlgAA3kRJREFU96tijN8p0W6GXFJC8ftXXfN2NVnh99qVuRNFndapY9vszxW1iIHu+W2cVLf32BjjZ6o1riQkgyGEQ0IIRxbeSHXgCj2kxCbK+XalDLQixcHbchOGPL7g5ylS7dqqcsH0y2vsy5/BjMler8gN9mrZz49I9abyHp6b1LIWX66x3S+LHn+lxvW2Fvy8hHRpWrMVZ1aXClQXBqlLBboLl60MIRxR+GTudXl4waKfxSoTiRQLIfSHEPYLIRxe4jVf+LceJF0m1jIhhAzpfzn/mttBuuyxbAZQl7iqehOg9v/t2Xpqwc+RmRkMtSg8WbIml/1VzR8p/Rqux+WVsuKLjBY9fnxxg1y/C6/4+VWM8fu1bDx34uJ/CzdHizLw/7SDEBaHEJaHEFaW+F8szOyp57OnJTp4bGv6X4sx7mXm+3w3TrwlqbvszN3yHgh8LYTwoA71B2AsxvirGtvWOsboxBjhplpOUubGsIXfl8YpP5l74XqRVK83r6H3/BDCQAhh/xDCESU+hwsD6ceEELoh/rC+6HG1zPdu9pU6MnQLX+uLacH3uBDCClLp0rzP1pNBTLq6tzAJ5ok1rvd/deyjlHpiAnuZ+f+/kDTnVbHiIP0n6+jPJ5m+CqTUtpoqhNAXQrh/COGwEv/DxUl0HR9P05lj24rPFbXIgupNNIctJn1RLnuJRyUhhCeSJhx4HKmG9ZIaV31AHbup5+z6H4se369Mu8Igw1juksJa/aB6E2DfD7vv1bEPSB/y+ZICfcDxVA+G3ZPL1q7FPUWPf9Hgevdj5sCaEMJ+zLwsv6wyg/dvAG8qePxkCrLhc0H/4wue3+e4xBjvDCFsZXrm4yeTJpUs3GahkmVLCoUQlgEnkTL1H0ma1KLWTIgHAPVe7lmLBSGEj5ImZM0bI9VALD6Z0W3uiTFur94MqP1/e7YKB9N3AA8IIdTzflU4MF9EyjS4uUzbvB/VEaQup9b3pfz/xm1Mn+gpNTB/GKn/eY28fxVm85zAvln5DQshrAZOJn1RfTj7zrReTj1/y1bp1LFt9LO0Vf9rkuaO3wF/TZroK19mYAVwdQjhyfWWgGuSVnx/6MQYoVo5ikL3APnEmF9Wu8KzaL28mt7zQwgHAC8iZaY/kpnJI5X05fZxd43tW6Uws/x3Mcaflm3Z/Wb7Wq81oaxWxYHpOxooW/pH0nxMkL5v1WJLnfso1khM4NUFj09g36uDG44HxBh3hBBuZvr3Pz6E0F/H/3VFuRNtzyHV4H4U6eRErVcgd8N4uhPHthWfK2oRA91z069JEyEWGgRWkj58/pYUiOwHXkE6u/6sWi+/CyE8FPgQ+5Z+qFU9/+jFbxKVFJdAKM4kz7+pF+6/3mDg1upNgH3P2hXXkK6mOPBcy1nAeo7V3gbXLV5vn2MMnEHtl+KXChR/K7ef/PvTk0IIoSAY+ESmP4jvIdWbL2Uz04HuJ1E50F2xbEkI4VTSpUqNno1t1Yfbh4GjCh5/D3hujPG3LdpfM9X8eo0xThRdXVnqddcMhZn3tXwBrWb/GrZR15UEZTTyPpb/Unq/EMKioitOOvH+VVXuJNq/AX9D7SeZCnXDILNTx7bRz9JW/a9JmkNijN8IITyPlFWZLwmxkulgd60ntpulqd8fcjoxRmh0bN/oehXf83PZ2K8nzRnRaDZwRwPdIYQBUl35vHpOJnSjVrzWZ6P4KtYLqL3cQym1JjPMdjw925jA8hJtCsdoscQ61fyC6WDsAHB/4Pd1bmMfIYTnkCZjPbLBTXTbeLpdx7bb/tdUQTdcOqTm2xtjvKXo9pMY48YY45tJb2qFgb0ZGbOVhBAeTQpENhrkhvped82u8Vd8BrI4Q7maWt/g9mvyfmo5c9rwsarzkrKWyk3u8sOCRQcCDy14XPja+1aFvhdmehcHtmuuz50rC/KfzC5Y16r32qOKHv9jjwS5oTvqd/5JblKTWksE1aqWL4HNmMxotu8v+1V53I73r4pyWXNXkyaRabSmZDeMefYretyuY9tV/2+S5p4Y41dJ2YGFNW6PJgW7213TtanveR0cIzT6ezT9PT9Xz/kjpKDlbEpedPqzuDhwencnOtFE3fb53ux5WWp9rc12PN3ssXTxsvEGvm+3Yjz9N6QTkkfOYjOd/h+GzhzbbvtfUwXd8CJVm+UmoziJNIFZ3qtCCE+rtF4IYSGpplHhG8EO0uRmzyOVL9kfWBJjDIW3pv4CzVVvyYBGf5d27WeuKA48P7nMz5XKuRROSPmQEMJy+NNM64W1434ZY/x1qQ2EEJ5Cylop9F3grFw/jiJ98VlY9Hp/Oe1RXLLgihBCcd1B1aYVZ9rb9X/c6veXbnj/ehfp0sq8e0kTcP5VbvkDSVcu9Rf9L1YtS9Rh3XBsJakpcnODDDMzS/jBwFdz5S56VS+PEZrlr4FTCx5H0txAryXN93E4aUy8oOhzONPujtZptuXjNFOz/1fm61i6kX1U3lgq/feBou3+hFQy9GmkK6HvBywq+h/u9u+WHT+26j6WLpmnYoy/DyH8PfDZgsUXhxAeUeGM2IuZGSDcBDwvxljxDGgdEym2wx+KHt+/zvVrvVTn7ibvp7jfXSvGeB5w3iw383XSpZF5TwY+GEJYTJroM69soDvGeFMIYQfTl5I9iTTJSD31uf+x6PHfxxjfU6njOe16zf8rqf/vzD0eBL4QQjgpxvilNvVhTogx7g4h7GZ6roGvxxjXdbBL9Zjt+8vdVR539P0rhHA4cErBou3AU2OMtcwt0E2fP9Blx1aSmi3G+NkQwv8DPsF0qbmHAV8JITy1zjq4XaHHxwjN8paCnyeBF8YYP1vDet32OVxcnmC/TnRiDis+vutjjJs60ZE6NXssXbxsWQihr87M42aP+c4mTZyZ92/AP9QwV1C3/Q9D9x1bdRkzuuexGOPngO8ULDqONMFXOc8p+HkKOKVakDvn4Aa61xK5OrSFfT66zk2sqt4E2LdOWK3r5R1T9LgZdXx7yTeZeXlQPjj9GKYncruX6pPwFWZ157dRU9mS3OSThUHxr9YY5IY2vuZjjBcArytYtAT4XAjhz9vVhzmkcGLe1R3rRf1m8z52T1F9bui+968NzMy8+Icag9zQRZ8/Od12bCWp6WKMo6T5FArHco8CvtRlCTD16NUxwqyFEB7MzM+r/6wxyA1d9jkcY5xgZjD2IZ3qyxx1Z9HjXvlfmW1MYEeJNoVjtNDAPgrHfBPUVyO6lMJYzk3A2TUEuaHL/odzuu3YqssY6NY/Fz1+S26ikVIK39B/Vscs6o+rv1stdX3Bz6tzE5zV6s+qNwHguqLHjy3ZqrzCYzbF7GeS7im5bJ8bChatCCE8iJmB5++XCNAVKxXorjWjeyUzL7+rJ0O6ra/5GOO/A69i+tKtRcCnQwgvaGc/usRsLkH9bsHPh4YQeuXLT63vS4QQDmZ6IkrY970K4EZm1lidzftXuX3Uo/jLRE3/i7lM8EPr2E87Ll/utmMrSS0RY/w48Gpmvrc+BtgYQhjsTK9mpVfHCM3Q0OdwTj1j4naVESlM9DowhPDQsi3nr0b/Ft8telyxNGoXmW1MoNR4rOF4QAjhIKYnSwTYEmOcrHX9EttbysyA9VfqyIDuxv/hrjm26k4Guue5GONVzJz478GkEiWlFF7SU8+EDS+rt18tVji46SNNnFNV7gTAi2rcxw+YWZ/wpFyN81r283BSdn3ejTHGbI37nUtK1emutT53XmGg+xEhhJXAwwuW3RZjvKXMusWXsNX0mg8hHMa+wfSWizF+mFQ7Mf9BPQD8Twjhpe3uS4cVn/yo6f8u56tFj0+dXVfa5oW5SaJqUfz+/p3iBrkTSIUDyKNDCCfUsvHchF2F75MR+F6NfSunof9F6v/sKXzt1PO6qVkXHltJapkY438Af1+0eC3wfyGEJSVW6Wa9OkZohkbHxI9jZkCpmtmM4epxTdHjV7RoP72soTFRjPHnwO0Fi54TQjiwab1qnXpiAguK2k5QOtD97aLHw3X056XMvJpxn/F6nfYrelzr//BS4C/r2E+7/oe76diqCxnoFsA7ih6/uUzQ5O6Cn1dVyPz+k9xkfs+cRd9a4RNFj/8hhFDLxBl/A6yoZQcxxnHgcwWL9gdOr617/EvR4/+ucb25pjjQvZ402U1eLYHuHzI9C3cf8A/MfN+rVJ/77qLHxSUDyvlnOjT/QS576v8xfZJlAfDfIYRTO9GfDim+9Kyey+0+xcwabX+Xm7il2x1BmiSqolxQ4fVFi4vfD/MuK3pcPClrOa9lui4+wBdjjL+rcd1y7i56XPV/Mfel6sw691P42jkwhNBftuXsdNOxlaSWijG+F3hj0eL1pAm0F5VYpVv16hihGe4uelzL53AA3lrnfmYzhqvHx4HdBY9fnSvPommzGRN9qODnZewba+hWtcYEXg0cVPD4s7nv/sWuAe4oeLwhhLCm2sZzV7wUv2fONh5wd9HjWr/XvoEUx6hVu/6Hu+nYqgsZ6BakCfpuKnj8MEqfuSssJXEQKaBWVghhFemNo6tmtY0x/oSZAc5jgIsqrRNCOBa4oM5d/XvR438JIVQsMRBCeA3wvIJF48BH6tzvXFEc6H4xabAEKWu56tnXGONeZmY8/k2VfRT6JbCr4PFfhxAOqLS/EMKrgZdX61crxRj/h3Ss8uUR+oCP5vo2H/yKlFmR99RaV8zNOXBhwaKlpMk9j6qnAyGE42sZbDXZhTV8SXs/KSietynG+NMybS9l5mD1L0IIf1tp47n3t+ITdcXvg424oejxWVX6sRQYZWZQuBaFdb8HSFmHrdBNx1aSWi7G+G/sO8H3s4BP1RhY6rgeHyPMVvHn8N/mJoiv5O3UX7aieP6N9XWuX5MY413M/H61lHTi5bBa1g8hDObm8pnLZjMm+neg8ET8K0IIb6pn5yGEpSGESnOHtUItMYFHAG8rWvy+Um1z9eA/WLCoD/h4pe+TuUTCS4DC1+J3Y4zV5qWqKMa4i/QdKe+51U7UhRCey77v29U0/D2sHt10bNWdDHSLXH2mdxYtfkuJppcXPf5gCOHlxWd4QwgDIYS/Br5FeiP5bdM62zx/x8w34dNDCJ/I1a/9kxBCXwjhRcAm4AGUnlG5pBjjN4D/Klg0SJpx/rTiQX0I4X4hhAuA9xZt5o0xxuLZq+eFGOMO4OcFiwoH1D+qcSJUmFm+pHhQXjajO1di4PMFiw4i/f0eVtw2hPDAEMIHgA/kFnX0NR9jvAI4iTRhJ6STTR8IIbyu/FpzQ+7v9v2CRU8JIVwSQnhaCGF1COHIglupLIN3MvN1sRr4YQjh7BBC2YyGEMIRIYTTQwhfJ11J0M4vsXcDBwBfDyG8oPhqmxDCihDC5cy8zHqCfS8n/5NcuaTigPJ7Qgj/GkKYcQlzCGFBCOHlwFeYPhkF8KkYYz11PMv5IjNPOr08hPCuUGJCsxDCk0ifPU8llfaoJ+O5+MTXpbn36+NDCEcVvXaqfcEvq8uOrSS1RYzxrewbIPoL4LIWXkHTbL04Rpi1GONtzBxbPRT4fEglAWcIITwohPC/wDm5RfWMiX/AzEzrs0MIbw4hPC6EcHTR5/B+df4axc5lZjD3ocC1IYRX5k6Y7yOE8LAQwtuAbdQ/mXSvaXhMlPuO9v+YORnt20IIXw4hPDmE0uX2QgiLQghPDSG8B7gVeFczf6Eq7s7dV4oJvAS4GrhfwVP/E2OsdIXwv7Lv6+xbIV31PkPupNn/AS8pWLwHeE3Nv0VlhbGcRcCXQwj7nMAIIdw/hPAvwBWkq4Nr/h9uwvewenTTsVWX6cjl9epK/wWcx/QkZY8OITwnxviFfIMY4xdDCN9guv7wEuCjwNtDCD8AssCBpIlm9su1mSJl0V7Z6l+gHjHGG0IIZzPzA/RkYDiE8F3gNlJQ4QTgkNzze0mXwv9nHbt6LfBI4BG5x/cnnUl8Zwjh+6QP1YNJJTmKAyf/E2P8APPb1yk9G3otZUuqtf1NjPGmMs/lZUhfwvJ/m0cBN4QQtpA+WPtIk1auYfrE4VbSCYt319HHposxbgwh/DmphE5+wP7uEMLiGGPxia255j3AEwsen5a7Ffs6sK5wQYxxb+7k1ldI/7uQ/m/PJ73X3QjcQnq/W0q6nO9YZl7C2G5nkt5XHkgaxP46hJAv27OSNEFL8Ynts2OMxRlaM8QYPxpCeDJwSm5RH+kSxr8LIXyHdMngfqQJeYrrL/6MJg0eY4y/DSFcyMyskjOB/y/3fn0n6UvHI5mZsX5hrm/7DHjL+BrwU9LfE9Kxu6RM2/WkE6AN6ZZjK0ntFGN8Sy4oVlhG64WkINpf1zE5Wkf06BihWd5E+r3zQcqnAb8MIVxLyuJcBBzN9HGBdPXlNbl1q4oxZkMIl5JKQ0D6rvlWSpdAyZC+uzYkxrgzhPB80u+Un7j6gaSyG/+e+257B+kq0oNI3+Xmwt+xVrMaE8UYrwohvJY0Js+fyHpG7vb73Hep35KO736kGMRDmBmfunOWv0M9riS9Dp5O9ZhA3lYqJI0AxBh3hzRf0tVMx0ceDGwKIfySNEn5fcBRpO+ThScBInBGjHFLw7/VTP9G+j6Uz3o+EvhmCOHnuX5Mksq0PpaUxQ/pb/QG4GN17Kfh72H16LJjqy5joFtAuvwjF0h4d8HifwS+UNT0xaQPssLg48HAn5fY7ATwyhjj/5U5cdtRMcaLcmfs/4XpN75+Zr4x5+0lvUFXKnVRah/35M4qfoaZl98dAJxYYdX3UeWDc574BvCqEsvrCXR/l/T3K36/q/q3jDH+NKSrE/6bmZNpHJ+7FbsJeDa1B9ZaKsb41RDCiaT/43w26Pm5YHemg11rqRjjaAjhsdRfozm//l0hhCeQvuwUlmjqI33ReUTJFQs2wb416lppE+n96SOk97AVlJ9PIAJvjjFWvDSzwMuB3wNnMP0+uYjKA9PNwPOafDVKhpSp8cKCZctIX0hK+TBwNmnwW5MYYwwhDJP+X46o1r4JuuXYSlLbxBjfEFJt7tcWLH4ZcF8I4RUxxtihrtWkB8cITRFj/FoI4SxSklDh96bH5m7Fvkv6fvh3de7qjaTgassnds+N8x9LylwtzLJfROvKl/WEZoyJYowfCCGMkb5HPbDgqf2praTFH6o3aZpIinNcRUraKxcTyBsDnp4rg1N5wzH+MJfc8HlmHsujc7dS7gVelZt/qSly710n5fpRmJX+EEonlt0JPAfY5wrKKvuZ1fewOvfVFcdW3cfSJSr0H0Dhm/VjQwjPKGwQY7yT9Ob/HmZeWlZoD2nA8Gcxxo+1oJ9NE2N8G2kg9f0yTaaALwNPbPTNMMZ4Nynr4WTgxxWaTpLOnj8xxvjabs9qaZNyl4JtLrN8H7nJQX5Y4qmaTlrEGP+XNNitFFzfTqpFeEKM8eZa+9YOuRI6z2Dml6rzQghv71CX2iLGeBbpi9f7SJfC/o7puuW1rL8rxvhXpAyO/2V6UtNyJklf6kaAo2OMn2yk342KMV5KGpB/mZmXihb6PvCkGGPNkwLF5Czgcblt763Q/CekDOUnNzsQG2OcJH0BeR3wmwpNvwO8IMb4qkbeQ2OMNwIPJ00e/HlSZt5O0hegpuqWYytJHfD3pO8dhf6GMrVuu02vjRGaJcb4blJCx48qNNtKmvz9yY1MmBxj3ElKDno+afLmn5LGsJU+IxsWY7yd9N32ZcB1VP6830PKAH8pKVt0TmvGmCjG+FXgQaSSbT+v0hxScPUTpBKM1U4aNVWM8Q+kZKXzmDnRYaE/kkpmPDLGeGsd276BlLDxj6TvjeXsJM3l8uBWBGJjjN8kndT5POX/jr8nvRc/PMZ4XYP7mdX3sDr31RXHVt0ldPlJc3WxXH3UJ5FqlC0jXdrya+DbuQ+KnhLShAyPI12WdC/pd/l+ri5dM/dzRG4/DySdIf096U35m7143OaTEMKDSMHEfE2xO0iXa37XExNzWwhhAemL0NGkKzIGSZPF/o5UxuanuS9n7ejLeaQvy3lHxRhvKXj+MNLg8jBSVtIdpNfoWBP2fX/SycEVpIycnaQvJd8r7EMrhTTHwWNIX4AeANxD+h2v77YTTfXohmMrSapfN40R2iWEcBxprHEQ6SreO4CbGg2MdYsQwgOBJ5C+px1ACs79nvR3/GEugUYNCiEcSnrdLCeNdaZI47hbSaXZbm7XVR0hhML9XBpjPLXguX5SotODSK+Fe0jf+a7J1aGe7b6PJ51EOIh01fBdue1/K8bYkoBwiT4cSorlHEa68vk3pL9D2/rQCt1wbNV5BrolSeoh1QLdkiRJksqrFOiW1NssXSJJkiRJkiRJ6mkGuiVJkiRJkiRJPc1AtyRJkiRJkiSppxnoliRJkiRJkiT1NAPdkiRJkloqhLA4hPD9EMKPQgg/CSFkcsvPCyH8OoSwJXfbUGb9Z4cQfhFC2BpCOKe9vZckSVIv6LpAt4NgSZIkac65D3hqjPGRwPHAs0MIj8s9d1GM8fjcbWPxiiGEfuB9wInAscBLQwjHtqnfkiRJ6hELOt2BEvKD4J0hhAFgcwjhi7nnLoox/lu5FQsGwc8Abgd+EEK4Msb405b3WpIkSVJJMcYI7Mw9HMjdYo2rPwbYGmP8FUAI4X+A5wGO8SVJkvQnXRfo7sQg+MADD4xHHnlkQ/2VJKmdTjjhhOJFN69Zs6YTXZF6wnXXXffbGONBne6H/pSUch2wCnhfjPF7IYQTgdeGEP4auBZ4fYzxD0WrrgBuK3h8O/DYSvtyfC9JKqdoPH3KmjVrTulUXyQ1ptwYv+sC3dCeQXAI4ZXAKwGOOOIIrr322ib/FpIkSeq0EMK2TvdBSYxxEjg+hLAfcEUI4WHAB4B/ISW2/AtwIfA3RauGUpsrXuD4XpIkaX4oN8bvuhrdkAbBMcbjgcOAxxQMgo8m1fS7gzQILlbTIDi3jw/HGNfEGNccdJBJPpIkSVI7xBjvBjYBz44x3pkb+08B/0G6QrPY7cDhBY8PA7aX2K7je0mSpHmsKwPdea0aBEuSJElqnxDCQblMbkIIS4CnAz8PIRxS0Owk4MYSq/8AWB1COCqEsBB4CXBli7ssSZKkHtN1gW4HwZIkSdKccwhwTQjhx6Qx+1dijJ8HLggh3JBbvh44EyCEcGgIYSNAjHEv8FrgS8DPgE/FGH/SiV9CkiRJ3asba3QfAlyaq9PdRxrIfj6E8F8hhONJpUhuAV4FaRAMXBJj3BBj3BtCyA+C+4GPOgiWJEmSOivG+GPgUSWW/1WZ9tuBDQWPNwIbW9ZBSZIk9byuC3Q7CJYkSZIkSZIk1aPrSpdIkiRJkiRJklQPA92SJEmSJEmSpJ5moFuSJEmSJEmS1NMMdEuSJEmSJEmSepqBbkmSJEmSJElSTzPQLUmSJEmSJEnqaQa6JUmSJEmSJEk9zUC3JEmSJEmSJKmnGeiWJEmSJEmSJPU0A92SJEmSJEmSpJ5moFuSJEmSJEmS1NMMdHfYZZfBwACEkG4LFqT7xYvhiis63TtJkiRJ9fre+edz2XHH/en2vfPP73SXJEmS5rwFne7AfLZiBWzfPnPZ5GS6v+8+eP7zYfVquOmm9vdNrZfNwugojI2lv/PwMAwNdbpXkiRJmo3Ljjtun2W//K//4pef+AQn33BDB3qkdti1YwdbLrqI7M03M3TUURx/5pksXb68092SJGleMaO7Q0ZG9g1ylzI2Bl/4Quv7o/bJZuHss+HAA+G1r4ULLoAzzkgnPjZv7nTvJEmS1KjRxz2u/JNTU1x74YXt64za5qbLLuOz69dzy5VX8rsbbuCWK6/ks+vXc9Nll3W6a5IkzSsGujsgm4V//ufa2z//+a3ri9pr82Y49NAU3N6zJ2XuA4yPp9fFU58K731v+lmSJEm9Y9tXv8pklUHcTR/9aJt6o3bZtWMH177tbSWfu/Ztb+P3P/tZm3skSdL8ZaC7Az70ofra79nTmn6ovbJZ2LABdu4s32ZiAt7wBrO7JUmSes23Xve6TndBHbDloosqPn/VC19oZrckSW1ioLsDPvjB+texTnfvGx2Fqanq7e67r7aguCRJkrrDrh07am77nX/6JybGx1vYG7VT9uabq7a59m1vY/ddd7WhN5IkzW8Gujvgl7+sf50HPxjOOqv5fVH7jI2lEiW1mppKwXFJkiR1t2pZvYVu/vSn+d/HPIYd113Xwh6pXYaOOqqmdvW8RiRJUmMMdLdZCI2ve9FFjQXJ1R1Wr4bBwdrbj4/D1q2t648kSZKa45Yrr6x7natf8Qozu+eA4888s6Z299SQ+S1JkmbHQHcbzSbInfeSl8x+G+qM4WHoq+M/bnAQVq1qXX9mK5uFSy6Bs89O906gKUmS5qPLjjuuofWm9uzh1quuanJv1G5Lly9nzZvfXLXd/WrM/O4GE+PjbL38cn544YVsvfxyT8hIknrGgk53YL5oVhDwhhuasx2139AQbNwIJ55YW+3tvr4UHM9mUwmTsTE4/PD03G23pQzx4eG03XbbvDnVEJ+aSpnng4OptM7GjbB2bfv7I0mS1Am//PznZ7X+jh/+kKNf8IIm9UadcszJJ3Pgox7FVS98Ydk2xZnfu3bsYMtFF5G9+WaGjjqK4888k6XLl7e6q1XtuO46Nr3mNcSpKSZ376Z/yRKuv+AC1n3gAyw/4YROd0+SpIpCjLHTfei4NWvWxGuvvbal+zj7bLjggtlvp78f9u6d/XbUOTt3wlvfmkrRhJAmnywUAsQIxx0Hb3oTvPrV0wHlQoODKRje7uByNgsrVpQ+eTM0BNu3w7Jl7etPrys8kdHJkxeSNFeFEK6LMa7pdD/UXu0Y3wNc9vCH1zbbeBkL739/XvjtbzexR+qkmy67jGvf9rbST/b389i3v52jn/vcsu3WvPnNHHPyyS3uZXkT4+NcsX49e0tkcC8YHOSka65hoJ5ajPPcxPg42774RbLbtjG0ciUrTzzR4ydJTVJujG+gm/YMhJcsgXvvnf12FiyAiYnZb0edt3NnCnBefTV87nP1TVRZqN3B5UsugTPOKN3fwUF45zth0SIDt7UolRnfiZMXkjSXGeien9oW6G6wbEmhk3/ykyb0RN1i9113seWii7j585+Hycl9nl90wAHc97vflV3/pE2bWHLQQa3sYllbL7+c684/n8ndu0s+f+Dxx7P2oou6IvO825XKjA99fWbGS1KTlBvjW6O7TZoR5IYUQNTcsGwZfOITcNlljQe5IQVJR0eb169qxsbK93d8HF73unS74IIUEF+xIgV0NVM2m4Lc2ez08Rwfn15eS3kbSZLU45oxiY+6ypKDDmL5E55QMsgNVAxyA2y56KJWdKsm2W3byga5AX67ZQufXb+emy67rI296j0T4+Nses1r2Ds+/qfjObl7N3tzy615LkmtY6C7DbZvb962Dj20edtS61WasPGb34Rrrpn9PsbHYevW2W+nVqtXp8zjciYnYdeu9LOB2/JGR8tf6dzukxeSJKl+v/vpT2e9jQXWe+sZu3bs4NvnnsuXXvISvn3uuezasaNs2++96U0N7+eem29ueN3ZGlq5kv4lS6q2u/Ztb2P3XXe1oUe9adsXv0gsM9CPU1NOQitJLeRklG3whjc0b1uPelTztqXWyNdcvuYa+MxnUimKXbv2nbDx+c9vzv4GB2HVquZsC6pPfjk8nH6PeuQDt6ed1rx+9rpqmfHtPHkhSZLqt7locsFGHPqEJzShJ2q14prav7vhBm658sryNbXLZHPX4n5HHdXwutVUmwBz5Ykncn2NE0ttuegiHv/2t7eqqz2tUmb85O7dZG+9tc09kqT5w0B3G3znO83b1mGHNW9bar58zeXCrOa8fFBzw4aU5X/33c3ZZ19fCj43Q3HN6EKFgfqNG1O7++6DPXuqb9fA7b7ymfHlap038+SFJElqvnurlKCoxQnnntuEnqiVdu3YUXaCyWvf9jYOf8YzmlpT+/gmnEAppZZg/cDgIOs+8IE/ld2opJOZ590unxlfKtjdv2QJQ0cc0YFeSdL8YOmSNmjWxMqLF8OxxzZnW2q+wprLxUHuQvns5v32m93+BgfTJI8bNzZnIspSNaMLFZYhOf74FKx/6lNr76uB25mGh9NJilKaefJCkiS1xuIDDpjV+gc8/OEdm3RQtatWM7vU84P5yyLrtObNb27Ja6JasL6wDMnyE07gpGuu4cDjj6+4zVZmnve6lSeeSCgz0A99fRzx7Ge3uUeSNH8Y6G6DU09tznYGBgx+dbNKNZcL5bObP/OZ+vcxMAAnnwznnAMXX5yCzWvX1r+dUmrtfz5Qv2wZvOAFtZ3IMXC7r/xJiqGh6WPY7JMXkiSpddbOctLAJ7/nPU3qiVopWyVzuVRm80F11JtcuN9+HPW853HSpk2ly6A0Qb3B+oHBwaqv71Zlns8F+cz4BYODf6p53r9kCQtyywealQknSdqHpUva4FWvgje+sfH1BwdToNDgV3erVHO5UD67+UlPSrdvfrP2fSxeDB/6UOnXQWFt7Xw97aGh5ve/sAxJtXrdvnYrW7s2nawYHU3HdNWqdEyXLateK72ev60kSWq+A449lrBwIbGWOm5FWpW5q+YbOuoofnfDDWWfL5XZfPyZZ3LLlVfWtP3nfPazVV8LE+PjbPviF8lu28bQypWsPPHEuoKljQTrly5fzpo3v7lkJriv3+rymfG3XnUV2VtvZeiIIzji2c9mYHCwaq10SVLjDHS3wdAQXHIJvOIV9a3X1wdnnJHKleSDX+pelWouFyrMbv7GN1Lg+jWvgRjLr7N4ccrmLhcwLq6tXTzxZTP7X1iGJJ99XLjvpUtTjfIXvhAe//j0e/3f/8GWLWkdg7UzLVu27ySdtdZKb1Y2vyRJaswzP/EJvvSiF9Xcfv/jjuMJ//qv3G/lyhb2Ss1ULWhdKrO5UpC4UC0B4x3XXcem17yGODXF5O7d9C9ZwvUXXMC6D3yA5SecUNPv0EiwHuCYk0/m8Gc8gy0XXcQ9N9/M/XJB2SUHHWSwtgYDg4Mc/YIXzFhW98SmkqS6hFgpujZPrFmzJl577bUt389f/EUK+NVq8WL4ylcMZvWKbBYOPRR27iz9fGF2c/HfdOfOlL3705/Cb34D+TJ5Bx0EBx9c+WRHNgsrVqT7YkNDKWO4lpMklbZTbZv5/hdmJW/ZUjlYW+5YzHez+TtIkvYVQrguxrim0/1Qe7VrfA/w1dNOY8d3v1tT2/4lSwh9fXUFKdV5P/vYx/jhv/7rPsurBSd333XXn4LESw85hACM33HHjIBxJRPj41yxfn3JiSEXDA5y0jXX1JTZvWvHDj67fn3Z50/atKmuDO3iYG2ewdrKmv13kKT5rNwY34zuNrrySli5Em69tbb2996bAoUGs3rDj36UMpmLLVwIL3oRrF9fPlhdKqu3VpVqa+fradey7VLZ2YUqlSEp7n/hxJal5Lf99KfDzTfDIYdU7998UW+t9EZfN5IkqTme/pGPcNlxx9XUdnL3bgA2veY1NQcp1Vk7rruOG97/fsKiRcT77gMg9PfzxIsu4oinPa3iuksOOojHv/3tDe972xe/SCwzMIxTU9x61VX7ZAyX0swyJNUmtjzwUY9i/4c+tObtzSe11EqfzetFkmSgu+3qLeFnMKs35AO7ue8uMyxcCB/8YOtOVlSqrV1YT7sWxTWjDzssLb/99pn1o6upNVh7333woAd55UKhRmqlS5KkzgoDA8SJiZrb1xOkVOdMjI+z6TWv2SejOk5O8t1zz+WQFp+syG7b9qeTI8Umd+8mW2sGFZXLkNSjWrD2qhe+0MzuMhqplS5Jqo+B7jY76qhUmqJWhcEsJ6frXpUCuzG29mRFpdraCxfCtdfCKafA/vvDccdVf63MJrs8r9ZgLXjlQrFGaqVLkqTOWnLQQezavr3m9oVBSmsdd69mZVQ3amjlSvqXLCkb7N5x7bXs2rGj5tfLbDPMoXqwFlJm9+HPeIZlOIo0WitdklS7vk53YL752Mfqa58PZm3enOr2nnEGXHAB/N3fpdsFF6RlK1akNuqMZmZV12t4OJUUKWXPHvjqV+HjH4d3vzu9ZtrxWskHa2uVv3JBlf+ehfbuhZ/8JE10W62etyRJaq0nXXxxXe37lyxh6IgjuOmyy/js+vXccuWVf5qU7rPr13PTZZe1qKeqRzMzqhux8sQTCRUGhr/dsqXtr5ehGoOx1TK/56NSE5cWuvumm/j2ueeya8eONvVIkuYeA91tdswxUOXzbYa+vpTtmq93XCqYOj4+XTqj3ESIaq1Kgd1WZ97ma2sPDVUPLt97b3teK7UGa/MswzGt1r/nfffBRRd5okuSpG5wwLHHcviznlVz+9DXx4EnnFCx1vHu/Ozk6ph8RnUp+ZMVrTQwOMi6D3yABYODhEWLyrZr5+ulWrA2zzIc+8rXSi/nDz/7mSe7JGmWDHR3wLveBVu2pNrEg4NwxBFw9NEQwnRwcHAwlXF4zWvg5S+vrba3WbGdUymw29eXnm+lfG3tiy+GE0+ECuNgIAVJX/QieO970+3ss5ubGZwP1lbrR55lOGYq/HueeWbl4+iJLkmSusOT3vUu1l1yCf1LlwLQv3Qpx5999j5BytDfz+Pe/nZ+8qEPVdyeGbGdVymjOvT1ccSzn93yPiw/4QROuuYaDqgyweOWiy5i144dfPvcc/nSS17SsszgasHaPMtwlHbMySdz0qZNHPW85/GAhzykbDtPdklSYwx0d8DmzfCkJ8Gdd6Yg1a23wi9/mWo59/en28qVKbj97nfDl76UApPVmBXbOaWycAcHp5e3o/Z0vrb2wx9e/fWyZw9cdVVrS+CsXQs33wyLF1dv246TAb0m//c89lhYUMNsCp7okiSps2667DI2veIVTO7aBcDkrl1seec7OfjxjycWDM7i5CSbX/c67vze9ypuz4zYzivMqM5ndvcvWcKC3PJWTkRZ3I84OVmxzZ0/+EHbyuAcc/LJPPvyyyu22W/1aiZqnbRnnsnXSr//McdUbOfJLkmqn4HuNstnXpYrQzIxAZOT8NOfpmBkLZnceWbFdlZhFu4556T77dvT8naqtz52Xisygw85BL7yleplOM4/34koy6l1Yk9PdEmS1Dm7duwoW4bk9q9+teTy3XfeWXGbZsR2h3xG9Zpzz+XYV7yCNeeey0nXXMPyE05oaz+q1cYuNxlqqzKD93/oQytmdv/4fe/jivXr2XHddU3f91xRbWJPT3ZJUv0MdLfZ6GgKZreCWbGdl8/Cfcc70n0ngrf11scu1uzM4PwJgHe+ExYuLN3mnHNaU3Yjm00lWZpdmqWdaj1x4YkuSZI654f/+q9N32attZDVegODgxz9ghdw/JlncvQLXtC2TO5Cs3k9tCozOF+GY+WGDft8AZncvZu94+Nses1rmpbZ3Y7SLO1U7eSFJ7skqX4GutvsJz9JEwI2U7tLZKi7FZZRqaVsSLFWZAYvW5bqTA8MlH5+chJOPz0FpGdbNzwf3H7Zy2D5cnjd61pTmqVdaj1x4YkuSZI6564f/aip21vz5jez5KCDmrpN9bZKtbGXHnpoxXVbmRm85KCDeOBjH0t/mUll9t57LxtPOmnWgembLrusbaVZ2qXayQtPdklS/Wqo/Kpm+v3vm7Odk0+GJzwBbr89ZXEODxvk1rR8FvXoaCqD87vfpcff+Eb1+t2tygyuVIJj1y745Cf3vdphcBDOOisF7mspAbN5cyq9MjmZtlkov+8NG9KxKPx/yWbTsRobSxnUw8PpREE3yJ+42LAhZdsXH8PBwRTk9kSXJEmds6CR7IL8uve7H4evX889N9/M/Y46iuPPPNMgt0o65uSTOfwZz2DLRRfNeL388F3v4pYrryy7Xqszg7PbtjG5e3fpJycnGf/1rxn/9a+55corWfPmN3PMySfXtf1KpYGufdvbOPwZz9jnf2bXjh1suegisjffzFDuOC1dvryu/bZa/uRFqd/Nk12S1BgD3W32gAc0ZztHHJEyYKVy8mVU8rLZlNFcLdDdqszgfAmOcsHuUiV9KgWnixXWv68kX5rltNNS27e+NU36GkI6NuWC6/lg+I03wh/+APvvD8cd156geOGJi61b4bDD0nJPdEmS1B0e9Jd/yZYLL2xo3b7+fh7/9rc3uUeaq/ITGRY6/swzKwa6W50ZPLRyJf1LlpQPdhcoF5iupFrplS0XXTTjmNx02WUzgsf5DPBSQfZOB8TLnbwwyC1JjTHQ3WYPe9jst7F0aX0Zt4XZqocfnpbddlv3Za6qtTqdGTw8nALIjSgMTpczOpraVZMvzbJ5M5x44r61wUsF1/OZ4hMTM0sPLV5cX8b5bBSfuJAkSd1j9fBww4HuBz7mMQ2t1+kAnbpHpzODV554ItdfcEHN7YsD09XUM2ljPdnf9QTEW6nUyQtJUmMMdLfZ8DC88pUQY+Pb6O+vPeM2H6ArF9hsV5BO3aGTmcGlAu0LFsDevdXXraVueKXSKIUGB9PvvWFD5Qkw88H1F7+4fKb4vfemWy0Z55Ikae4aGBxkyaGHsnv79rrXXXPuuXWv0y0BOnWPTmYGDwwOsu4DH2DTa15DnJqqmtldb83woaOO4nc33FD2+cLSLLVmfzdSDkWS1P0MdLfZ0BA88pH8/+zdfXxUZ53///dJCJBMBtsisS1NaCxgSy1SyXbtGpVoEUjdRaRruunuVrf+dLGrNPjtl2L87uzsr9Au2qb4reJ2cbX9aWp26Y1ZC7RViZqtd6GitLQlrCGhQh1KqwyTACE5vz+uTAlhbs7MnLl/PR+PPELmus45F5lkuPicz3w+2r078WPLy01g0GnGbbxSDomUhUDhyGZm8MRA+6FD0iOPxA9QT6wbHuldCr/+tTR5snTqVOxzlZSYG03xsr/DwXUnmeJOMs4BAEBhKz///IQD3clk2xKgQzTZzAyuWrhQK3bu1MCOHXrxoYf0xxhZKrFqhkd6p0IipVmcZn8nWg4FAJAfCHRnwS23SJ/5TOLHXXqp9OlPm0C5E05LORCkQyaND7QHg9Jjj8U/Znzd8FjvUohlfGmW//ov58H1ffviz3WScZ4ulCYCACA3TLvsMr32/PPOJpeWqvoDH9Al112X8HUI0CFXlXk8umzlSl30nvfo8YaGqPOi1QyP9U4Fp6VZnGZ/J1IOJVsoTwQAiSvJ9gKK0c03mzrbiXrhBen2201Dwe7u+POdlnLIZpAOxS1czsTrNYHliTweM7Z1q/Sd70i33SZdd50J7jr52a6okKZMkW66Sdq0yWSQ19efaYwZSzi47mTuxIzzTHnqKamqSlq1Stq40dxA+8xnzJ9vvVW6+GJnrxUAACB1CTX8GxnRwaee0uMNDfrv22/XcAJ37/MhQIfiFq4ZHsnEwPRgIKBn1q3T9o98JO47FVZ0dal2+XJNnz9ftcuXa0VX1zmleuL9HobHvTGyyqXYWeeZ8MznP6/HGxp0oLPzjYD/4w0N2tfentV1AUCus+xUikUXiLq6Orunpyej1ww3whsakkZGEj/e641fbmTLFhMYdJK5umkTGd3InuPHo9cNr66WbrghsQzuKVOk979fWrkyct3xYNDcMIpW1qeyUtq+3QTF482VnP0+uu2pp6QlS+LPq6iQfv97ShMBKF6WZe2ybbsu2+tAZmVjfy+dm5HqVEl5ud7/r/+qqoUL4859Zt26mGUcapcvJ6MbOWHoyJGYNcMT+X1J5Oc62nnH17AfDARiZp2v6OrKWgmgR977Xp08ejTqeDbXBgC5Itoen0C3srcRfuop6c//PH5N4UicBKedBOik7ATpACec/gxHcscd0l13RR+fWAJl8mRTu3vNGukLXzj79yE8d3jYNJ8MmzpVKivLfEPXYFB605ucN7W9/36T4Q2kanypHMrjIF8Q6C5O2drfS9JzDzyg32zalPBxkzwerdi5U2Vx3kqWywE6wKl4P8cTeWbOVONjj8X9/QiLF2SXnAXEM+1/vvc9/Xzt2phzapYsUf2992ZoRSh0lMhBvoq2x6dGd5YEgyZLNZkgt+Ss3Ei4LES0esbjaxYT5EYuclpnfqKpU+OXEpnYGHP27MjZ3xPn7t0rHT0qTZ8uzZsX/Zh02rjReZBbkp54gkA3khcObu/cKT36qPl3Y3DQ/BuyZk3mb/QAQC4bDASSCnJLkj06qoEdO3TZypUx54XLQjipVwzkqni15icafOUVPdbQoEWbNzt654OTxpxzm5tVvXhx3IB4Jv3885+PO+d3P/6xhkMhx0F/IJpYdfGzdbMHSBWB7ixJNoAXNmWKs5rAE4N5E8tCZCNIBzjltM78RCdOSNdfH3/e+MaYbs5Nt//7fxObv327ZFlnvq6ulj77WelTnyIbF7GF380wMmKC2+OFfzcbG3lXEACEJRq8G29kaEjBgQFHc3MxQAckIl6t+YnskRGdDoXUtWqVo3c+OOUkIJ5RDuqajgwN6T+vueasxy5YsEDvbWsjExeODQYCcevi828K8lHOBboty5oq6ceSpsisb6tt2z7Lsr4o6c8lnZL0P5I+btv2HyIcf0BSUNKIpNO5+lbVZAN4YbZtgtRO5FKADkhEuBFkor8rU6eaLObxP/eFVHJhaCi14w8eNI1tv/AF6fvfJxsXkQWDJogdr3TQ6Kj53eLfGQCxFMseP9Hg3Xil5eXy1tQ4np9zATogAd7aWh3dsyfh48LvfLjoPe8pzHILpaVJNfF6bfduPd7QQCYuHIt3Y3Z3Wxv/xiAvlWR7ARGclPR+27bfIWmBpKWWZb1L0tOS3m7b9nxJ+ySti3GOBtu2F+TqBlg6E8BLVksL2XMofE1NpkxCok6cMO9gCAZNU9abbpKqqqTVq03Zj9tuM7W/u7tdX3JGXHyxO+c5edI0xT1+3J3zobA4feeRk1JaAKAi2eN7a2uTPtYqKVHN0qUurgbIXQtaWpI6bmRoSP07dujxhgYd6Ox8o9TC4w0N2tfe7vIqM+9PUwws9qxfr6EjR1xaDQpZvBuzx1K4cQtkU84Fum0jHHYpG/uwbdt+yrbt02OP/0zSJVlZoEuSDeBJJsD9hS+4ux4gF4XrzHu9id0Y8njMux5mzjTB7fZ2E/wOl14Ihc5kq+ZjkPeRR9w719CQCWgCEzl955HH46yUFoDiVix7/GSCd9akSZrk8WjR5s3U3EXRCNeaj2RWY6NKy8sjjllTpuiVZ56JOFYIQd7LPvQhlb/lLSmdI5USSige8W7MTkvhxi2QTTkX6JYky7JKLcvaLSkg6Wnbtn8+YcrfSdoe5XBb0lOWZe2yLOuTMa7xScuyeizL6jmShX8Mxwfwpk51dkxZmZm/fTvZ3Cge4TrzmzZJd9wh3X+/+Whpif67Mzws3XuvCWZPrCs8XrjkQr555zulj37UnXONjJzJxg1nwK9daz7HK1mBwub0nUclJc5LaQEobune42d7fy/FDt5FVFqqS6+/Xit27nTUYA8oJHObm7Wiq0u1y5dr+vz5ql2+XCu6unTNP/2TrChZYfbJkzHPWQhB3hU//KH+7J57zm6yk4BwJu5gIKBn1q3TkzfeqGfWrdNgIODmMpHn4t2YTfZdF0C2WbZtZ3sNUVmWdZ6kxyR9xrbt58Yea5VUJ+kjdoTFW5Z1sW3bhyzLqpJ5K+RnbNv+cazr1NXV2T09Pa6v34njx02gbe9e6ac/lX75S+n06XPnTZpkyi/cfz9BbiAs3ChvdNRknk6dajK3Eyltd8cd0l13pXed6fKb30gf+Yh0+LDJzE725fxv/9a87jz6qAlaDg6aAGdJibkhRw3v4hQMmndFRLvhwc8I8oVlWbtyudRFMcrEHj+b+3tJGjpy5I1GkZXV1Tr4wx9qNEKTjUkej6uN9YBCEdi1S12rVskeHdVIAg1qps+fryUPP5zGlWXWcCikxxoadDqBpkW1y5dr+tvfHrHRIDW8Md6+9nZ+TpC3ou3xczrQLUmWZfkkhWzb/pJlWTdL+ntJH7BtO0ae5hvH/pOk47ZtfynWvGxvhMNiBRW8XpPVSpAb+WJ888fqavPYwYPuN4IM3yx6/nlp82YT6HbK4zGZ4oXQRK+7W7ruOlN3O1EVFdEz3+O99oSf5+eek15/XbrgAunKK/O72SfOmHgzqaLC3ES64QapocE8z/y7hFxHoDs3pXuPnyv7+7CJQbvS8nJZJSVatHkzmdzIO4OBQEYaQQ6HQtr/H/+hX30p5n/nz1K7fHnBNdAL7NqlH3z847IdZvIs3bpVO264Ier4iq4ulc+YEfc8mXqekV3jb8xOG3uenfx8ANmWN4Fuy7JmSBq2bfsPlmWVS3pK0r9IOi3pXknvs2074nsRLcvySCqxbTs49uenJf2zbds7Yl0zlzbCE4MKZMwhH038OR4vXT/TW7aYJpMJJDsU3A2kw4fda1QZFutmQPh5Hh4++wbD1Kmm1BKvW4UhfDNp/35Ti5vgNvINge7ckOk9fi7t78OGQyEN7Nih4MCAvDU1qlm6lExu5J1MZ4A+s26dDnR2Op7vNIibb/544ICeuP76uPMu++hHNXLiRMzvmZObAWT6Ash10fb4k7KxmDgukvSgZVmlMjXE/8O27e9ZlrVf0hRJT1umVtXPbNv+e8uyLpa0xbbtRklvkfTY2PgkSe3xgty5JlyPeGJQwbZNIK+31/2MWMBN4SaP0codhAPRjY3uBpmdNs4LCwdiCylgd9FF0pNPSkuWOJs/aVLkUknjhUJnaniPF+t5PnHCfLj9HCM7KisL410PALKuqPf4klTm8eiylSvfyJLc/5//SZYk8spgIBAx+CmZRpDVixe7HmQOjtWbduLtn/50QQa5JelNl16qutbWqN9/SaqYOVN/6vPpyRtvjHmuY3G+p9l4ngHALTnXjNK27d/Ytn21bdvzbdt+u23b/zz2+Gzbtqtt214w9vH3Y48fGtsAy7bt39q2/Y6xjytt247+r0AOCwcVPvMZ6cc/lq65xpQDuPVWaeNG6R/+wWRtdndne6XFi6Z90XV0mEzueNxuBOm0cV7Y8LAJDBeaD37QBLsnRbmNWVoqXX219MlPxg9yS6ZUxaFD5/6sO3me87XZJwDAfezxjX3t7Xq8oUEHOjt1dM8eHejs1OMNDdrX3p7tpRUtGvY5F6/RYzoaQXprax3P7d+2zfXr55Jw8843X3PN2QOlpXrP/ffrw089JSn+9+z4wYN69H3v05PNzfrjgQPnjGfjeQYAt+RiRjckffWrJrAdycmT5mPJEun3vydbMlPCtYh37jy3ad+aNZRpCHOaWR0tUzhZTU3meUjE3/6t9N//7d4acsUHP2jqZccqN7Fli/Ttb8d/rgYHpYcfNjcGpkyRWlqk7dudPc9uP8cAAOQzsiRzz8TyDOGbD5RniCxednW8TOFkLGhpcVy6JBghaFtoymfM0Ae/8Y2Yc+J9z06+/rok6cSrr+qJ66/X2/72b7Vw7do3xrPxPAOAW3IuoxsmezJakHu8wUHpwQfTvx6Y7PmZM6XVq6X2dlOWIdy8LxQ6U8bh+PHsrjMXOM2s9nhMANYtXq+52ZCI3/7WvevnmvA7Q+66y3yeeEOsqcncrHFieNh8PnnS/IwvWWIajMZ7nt1+jgEAyGe77rorpXG4K96Nh6EjEUvGF7V4mcLTEsi+dqqiqkp1ra2un7eQJfo9e+mhhxTs73/j62w8zwDgFgLdOWjdOudzn3gifeuAMb4WcTi4HQllGgynAdSSEjPXTfX10uTJzucXc/+n8I0Br/fM92HqVGfHDg6aoHe85zkdzzEAAPnq97/4RczxV37+8wytBBLlGZKxoKUlpfFkzW1uVvUHPxh/oqnjD50pc1K7fLmmz5+vKeefH3P+Tz//+Tf+nK3nGQDcQKA7B734YrZXgPGc1pymTIMRKYA6nsdzZk46yu78xV84n/uBD7h//XwSbn67aZN0xx3S/PnOj/3BD848zxMD5FOnRn+OqW8PAEAUBOkyivIMiYuVKVzX2prW0jsLHWSDVc6albbr56PyGTN07YYNWvLww3FfX4IHD77x50SeZ2rcA8g11OjOQZdfLsVJ+HjD9dendy1wXnOaMg1nhAOo4RrRl1xiHn/55cj1ot20ZIn0yCOSbceeN2mSafRa7MIlTiQp0Xchjn+e9+6Vjh6Vpk+X5s07+zkutvr24b/vc8+ZWukXXCBdeaX5nni92V4dACBbqq65Ri+PNYuL5C1sTDLKW1uro3v2RB2nPENkc5ubVb14sXa3telYX5+m1dZqQUtL2uvLV1RV6YKrrtJrMZ6zGe94R1rXkM/seP85mpDZ5eR5LsYa94OBgHa3tSnY1yfv2Pekoqoq28sCMI4V9wWvCNTV1dk9PT3ZXsYbDh0y9aDjKS+XAgGaUabbli3SbbfFD3Z7vea54/nIrmDQ/P7EyxTm+TrX298uPf+8s7n33++sl0B3tyn9MzISvfRPIT0X4b/v8LCp5R82dapUVlZ4QX0gH1iWtcu27bpsrwOZlWv7e8kESB5vaIg6vqKri2aUGcTzkX94zpK37cMf1h96e6OOn/e2t6nx0Ucdn68Yn4uJgf2wQg7sA7ks2h6f0iU56OKLpa98Jfr45MkmIPTUU4URGMp18WpOp7sUBxKT7dIp+exjH3M2r7xcuvnm+POKrb79+L/v+CC3ZL6maS0AFLdsln3AuXg+8g/PWfLOu+KKmOPnX355Qucrthr3NK8F8gelS3LUpz8tfeQjpm7uSy9Jb32rtHChdORI+ks/4GzhoGhjownIhUJSRYXJUL3hBqmhgecj12SzdEo++9SnpC98wTSajGTyZPOxfbuz71+x1bd38vcNB/XD5WIAAMUlW2UfEBnPR/7hOUvOgpYWHejsjDmeiGKrce8ksH/thg0ZWg2AWAh057ALL5S++c1srwLSuYFTgqW5b3ztaTjj9Urf/760dKkJdp8+faZvzaWXSpddJi1fLjktf1hs9e2d/H0LJagPAEheuEEccgPPR/7hOUtcOBs+UlbyBVddFb+G9wTFVuO+2AL7QD6jdAngUDhwetdd5jNBbhSi+nrplVekz33OZG9PmmQae/b1mSD47bebGujd3fHPNWdO5PIxE5WUmBtH+c7J37dQgvoAAADIL3Obm7Wiq0vTr7rqrMdf27NHjzc0aF97u+NzxcsATzRDPNd54wTuCy2wD+QzAt0AgLPYtvTVr0qnTpmmiuMlUms61fr2waBpBrt2rfkcr8FotsX7+0qFE9QHAABA/rFtO2omdiK1ppOplz4YCOiZdev05I036pl16zQYCDhfeJYVW2AfyGcEugEAZ0mk1nQskRqDVlRIU6ZIN90kbdpkSgLV1585JhzcvukmqapKWr1a2rhRuu0255nk2TL+7zt16tljU6fSBBUAAADZ5WYTyXCGeO3y5Zo+f75qly/Xiq4uzW1uPmfuvvZ2Pd7QoAOdnTq6Z48OdHYmnEWeTTRCBfIHNboBAGdxs9Z0IvXtu7tNpvjIiDQ4eO71JDN+6FDuBovH/3337pWOHpWmT5fmzaOuPwAAALLL7VrTTuqlDwYCEWuDSyaLvHrx4rwIFNMIFcgPBLoBAGcJ15qOFexOpNa0k8ag4XIo8cqThDPJc7nRKI1QAQAAkIuy0UTSSRZ5vjQXpREqkPsoXQIAOEs2ak07KZciOc8kBwAAAHC2bNSadjuLHABiIdANADhLNmpNOymXIiWWSQ4AAADgjGzUmvbGyRJPRxY5gOJF6RIAwDkyXWvaSbkUyf1McgAAAKCYZLrW9IKWFh3o7Iw5DgBusWzbzvYasq6urs7u6enJ9jIA5KFg0ASDe3tNsLapyWQ8IzHBoDRzZvQa3R6PCXJv3SoNDPD9BuCcZVm7bNuuy/Y6kFns7wGkajgUUv/27Qr298s7a5ZmLVumMo8n28vKS/va2yM2pKxrbdXc5mZJfL8BJCbaHp9At9gIA0hOd7dpoDg6ajKRw8HYbdtMRjQSM/H7WVEhjYxIN9wgNTRI1dXmz3y/ASSCQHdxYn8PIBWBXbvUtWqV7NFRjQwNqbS8XFZJiRZt3qyqhQuzvby8NHTkSNQscr7fABJFoDsGNsIAEhUrA9nrNWU/3C7vUQyOHzcZ8vv3m1rc4TIpmfx+k6UPFBYC3cWJ/T2AZA2HQnqsoUGnI9TUm+TxaMXOnWQauyjd3+/BQEC729oU7OuTdyzAXlFVlcqSAeSAaHt8mlECQBI6OkxmcSSjo2YciauslG65RbrrLvM5HLxO9/c7GJS2bJFuukmqqpJWr5Y2bpRuu80E2Lu7Uzs/AAAA8kP/9u2yo2w87dFRDezYkeEVFbZ0fr/3tbfr8YYGHejs1NE9e3Sgs1OPNzRoX3t70ucEkNtoRgkASejtjd44MRQyGclwTzq/3+GSKSMj0uDgueeWzDhZ+gAAAIUv2N+vkaGhiGMjQ0MKDgxkeEWFLV3f78FAIGJdcEnqWb9e1YsXp60BJ4DsIdCNokV5AqRizhxTIzpS8NXjMWU34J50fb+DQRPEjtYEMyycNX7LLcldp1CMf92srjaPHTzIaygAACgc3lmzVFpeHjH4WlpeLm9NTRZWVbjS9f3e3dYWd/zaDRuSOnchosQLCgWBbhSVcJBm507p0UdNI7vBQRMoW7OGpnZwrqnJ/MxEUlJixuGedH2/Y5VEGY8s/XObhY7HaygAIJsI0MBNs5Yt07MbN0Ycs0pKVLN0aYZXVNjS9f0O9vXFHD8WZ7yY7GtvPyv7PVzmpa61VXObm7O4MiBxBLrzTL5n02Uzi5ryBHCT12uCeuMDfx6PCbpu28bPkNvS9f2OVRJlvHRk6efTu0riZb7zGgoAqcn3QO1wKKT+7dsV7O+Xd9YszVq2LGPN+gjQwG1lHo8Wbd6srlWrZI+OamRoSKXl5bJKSrRo82YaUbosXd9vb22tju7ZE3V8Wm1tskt2JJuvi4mgxAsKjWXbdrbXkHX50pU9XjZdOOCTq9l0E9efyTUHg6ahXLzyBB6PtGkT5Qng3PHjJli5f78JhDY1EeRLJ7e/31u2mIaT8YLdXq+7Adxsvh4mw+n3iddQ5KJoHdlR2PJlfy+dG6gNy5dAbWDXrqgBqqqFC9N67cFAQI83NEQdX9HVRYAGSRsOhTSwY4eCAwPy1tSoZunSnAxUFgq3v9/ZfH3I5utiop5Zt04HOjujjtcuX06JF+SkaHt8MrrzRL5n00VafybXTHkCpEtlJUG9THL7+x2rJIqUniz9bL8eJsNp5juvoQCQmHzPpBsOhdS1apVOj/tHIlxnt2vVKq3YuTOtgUFq8CKdyjweXbZyZbaXkRa5mG3s9ve7oqpKda2tUW8kpuu1Nduvi4mixAsKDYHuPOE0UJurDdNird/JmlMt2ZLN8gRAvsqn0hrJilQSZfJkU+Lo6quluXOlqirpxReld7zDnb9/qq+H2RCrGeh4vIYCQGLyPVDbv3277Cj/qNmjoxrYsSNm4CrVki0EaIDERco2fnbjxpzMNk7V3OZmVS9erN1tbTrW16eKiy6Sbdvq6+zUq3v2pKVMVKqvi5mW7RIvgNsIdOeJfM+mi7X+eGt2owGa0yANTQQBI1JpDTeaDYaD5889J73+unTBBdKVV2Y3iF5fb7Ko77xTuvde89jIiNTTYz4kd5stpvJ6mC3xMt/DeA0FgMTke6A22N//RqbiRCNDQwoODEQ91o3a2gRogMSkM9s4V3sNlM+YoWs3bMhYPf9UXhezYUFLS8zSJQtaWjK4GiB1JdleAJwJB2rjydVsuljrj7Xm8W/xjxQYCoXOzDl+PPr1m5pMACYaj+dMZmeulQwAMi3S753T37VYurtNrfzPfMbUcX7oIem++8zXM2eacbcEg6au9Nq15nMwGPmxMNuWvvpVaXhYOnXq3PO58fcPS/b1MJvCr49eb+S18xoKAMnxxgnE5nqg1jtrlkrLyyOOlZaXy1tTE3EsXsmWoSNHHF0/XgCGAA1wNifZxsnY196uxxsadKCz840A8uMNDdrX3p7KcqMaDoW0f+tW/eqee7R/61YNx8loc+s1x4lkXxezJVziJZJ0lngB0oVAd56IF6gNy9Vsuljrj7XmREu2RBMpSFNRIU2ZIt10kwm6HTqUm03ggExzUlojUeOD5ydOnD124oR7QWTpTED9ttukjRulW2+Vzj9fevObpdWrzWO33XZ2cN2t1xonkn09zLZw5vumTdIdd0j3328+7riD11AASFa+B2pnLVsmK8o/alZJiWqWLo045qRkixMEaIDEpCPbOJNBZMmUXnmsoUG/3LBBL/z7v+sXPp+2XnutBr7//ajHuPWa40Syr4vZNLe5WSu6ulS7fLmmz5+v2uXLtaKrKy8aIgMTUbokT0SqIzteOhqmuSnS+p2s2c2SLeEgTUeHmTt7tgko5eL3C8imdJTWcBJIdqM+daRGj+EM7ZGRM3+e2Pwxk+Whkn09zAU0XwUAd2WrWZpbyjweLdq8+Zx6v1ZJiRZt3hy1BIKbJVsm1uCdNlYyIde/d0A2hLONIwW7k802zmSvgUilVyTJHhlR9+rVuvr223XFxz52znGZLBOV7OtitoVLvAD5jkB3HpkYqL3kEvP4yy/nR9A2mUCz2w3QCNIA8cX6vZs8+cxrTyKcBJLdCCI7zcwOCwfXM91skRtvAICwfA/UVi1cqBU7d2pgxw4FBwbkralRzdKlMYM5btfWJkADODNr2TI9u3FjxLFks40zGUTu375dI6dPRx3/1Re/qEuvv/6c189M1/NP5nURgDsIdOeZfA/UJrp+GqABmRfr9+7UKVOq4h3vSKxMhZNAshtBZKeZ2WHh4PrnP5/515p8fz0HALgn3wO1ZR6PLlu50vF8mp8B2RH+XX3poYfOGbts5cqkArGZDCIH+/tlnzwZc06kDPJsvOYk+roIwB3U6EZOowEakHler3T33dHHjx9PvJ62kz4DbgSRnTbuDQsH13mtAQAgc6itDWTHYCAQMcgtSS899FBS9bQz2WvAO2tW3DmRMsh5zQGKh2XbdrbXkHV1dXV2T09PtpeRsmDQvA2+t9cEe5qaTGCmEBw/nr8lW4B8EwyaRo3j61xP5PGYBoSJZCR3d5sA+fDw2Q0pp06VyspMEDnVZoZO1j6e12tKiIRfQ3itAQqPZVm7bNuuy/Y6kFmFsr+XTE3a/u3bFezvl3fWLM1atqxg3v4+dORI3pZsAfLRM+vWxcxsrl2+PKl3l+xrb4/aa8DNhobDoZC2Xnut7JGRqHNi/R14zQEKR7Q9PoFuFcZGOBxAitTYLNXAEYDismWLdNtt8UuA3HGHdNddiZ07HEjeu1c6elSaPl2aN8/dIHJ3t7RsWeyMc14jgeJBoLs4FcL+XpICu3ZFbWhWtXBhtpcHIM88eeONMcuMTJ8/X0sefjipc2cqiDzw/e+re/XqqOMruroIXgNFgEB3DPm+EY6VwTgxWxEA4lm7VorSo+YNFRXSDTdIF16Ym+8gOX5cuvNOqa1Nsizp5Emz5pERs+6GBjK0gWJBoLs45fv+XjKZi481NOh0hDvPkzwerdi5s2AyuwFkRryM7snnnaeL3/teLWhpUUVVVQZXlpgXvvlN/eqLXzzncbczyAHkrmh7fJpRFoCODpPJHcnoqBmn4RkAp5w0jhwclP7zP6WhITN3zZrcyo6urDR1xr/whTOlSHKx/Eghl5wCAKSmf/t22VE2+fboqAZ27KDRGYCExGvKeOoPf9CBzk4d6OzM6aDxFR/7mC69/vqcLEMyGAhod1ubgn198o6tK5dvGgCFhkB3AejtjR6QCoVMgAcAnGpqMoHreIaGzOfw609jY+69g6SyMvdu9IWD2zt3So8+akqoDA7m5g0DAED2BPv7NRL+x3aCkaEhBQcGMrwiAPku3JQxUj3tiXrWr1f14sU5ETyOpHzGjKTqiafTxFrlR/fsyfmbBkChKcn2ApC6cPZlJB6PyWIEAKe8XhNs9XpNuQ+nwu8gQXTd3abU1OrVUnu7aco5OGjGQiETBG9sjF1fPJcEg6am+9q15rPTJqAAgPi8s2aptLw84lhpebm8NTUZXhGAfDcYCOjVPXt0/hVXqLK6WpOmTYs5f3dbW4ZWlv8GA4GoNxB61q/X0JEjGV5RcgYDAT2zbp2evPFGPbNunQYDgWwvCUgIge4C0NRkMgIjKSkx4wDgVDAovfiiaeh46lT015eJQiHp3/6NgGc04SB2MHgmuB1Jrt8wCAe3b7pJqqoyQfuNG00D05kzTTAfAJC6WcuWyYryj7BVUqKapUszvCIA+Wxfe7seb2jQgc5Ovf7CCzp+8KBOHzsW85hjfX0aDoW0f+tW/eqee7R/61YNx+tYX6Ti3RTIh5sG439Gwtnojzc0aF97e7aXBjhG6ZICEM6+bGw0AZJQyGRyl5SYx3OpjACA3NbdbV5LRkZiB2Oj+fnPpeeeowRHJLH6KYyXyyWnYv185HIJGwDIR2UejxZt3qyuVatkj45qZGhIpeXlskpKtGjzZhpRAnAsVrZxLJOnTdNjDQ1nvQY9u3GjFm3erKqFC9Ow0vwV7OuLOX4szni2xctIz+UyNsB4BLoLRH29CSwk2nRtfCO06mrz2MGDNEUDitH4jONUFHPAM9Zr6nPPxW7wGZarJaec/nzQBBkA3FO1cKFW7NypgR07FBwYkLemRjVLl8YNctMMDcB4yWYT/37XLo2O6xUQ7hvQtWqVVuzcWXQ33GK9tnpra3V0z56ox06rrc3UMpPiJCM912qiA5EQ6C4giTZdC2fmhbPAx6MpGlB8nGYcO1VsAc94r6kjI9LUqaYudyy5WnKqEDLSASAflXk8umzlSsfzaYYGYKJ42caRzGps1Ms7d0Ycs0dHNbBjR0KvTfku3mvrgpYWHejsjHr8gpaWTCwzafmekQ6EUaO7SI3PzIuUYZiPTdGAYhWumXzbbdLNN0stLcnVye7tdZZxLEmTJ0tTpkjz5kWfU0wBTyevqSdOxA5yezxnSlHlYha805+PXM1IB4BiUCjN0AAYbjUG9MbJJq5ZskS1y5dr+vz5ql2+XCu6ulRx4YVvZHBPNDI0pODAQFJryUdOXlsrqqpU19oacU5da2vOl/2I9zOS6xnpQBgZ3UXKaWZesWVkAvkmnEU8PHx2EHXq1MTflTFnjglSxgtmTp4s3XuvCap/5zsmwB7pmGIKeDp9TZ06VbJtadIk8z2rqDCZ3jfcIDU0OCs5lS1Ofz5yNSMdAIoBbz0HCoeb786Il228cN26cwKx3lmzVFpeHjHYXVpeLm9NTUJryGdOX1vnNjerevFi7W5r07G+Pk0bK2+S60FuKf7PSK5npANhBLqLlNPMvGLKyATyTayayeHs4UTqZDc1mWzwWCoqpCefPBM8b2oyAfVIiing6fQ19cQJ8/2aNy+xfgpOjK8Pno4+C7Gea4kmyACQC3jrOVAY3G4M+PL3vx91LFq28axly/Tsxo0Rj7FKSlSzdKnj6+e7RF5by2fMyMsbiuGM9Eg/d/mQkQ6EUbqkSIUz8+IppoxMIN84ySIOvyvDiV//2mQXx1JSIi1YcObrcKkNr/fMa0qul+BIh0ReU+fNM++Suesu8zmV71G4bM1NN0lVVdLq1dLGjSbLfuZMk/HvlkjPdUWFKWFz003Spk3mpgp9HQAge3jrOVAYnGQQOxUraC5J1YsXR3y8zOPRos2bNcnjUWl5uSSTyT1p7PFiakSZjddWt8rWJGJuc7NWdHWdU8aG/g7IJ5Zt29leQ9bV1dXZPT092V5GRgWDJggSr4av1+s8GxRAZq1da4Ka8dxxhwmqxuL0NcHjMQHNieWMjh83AXW3s5TzRTZeU8Nla0ZGpMHB9F8vrNifa+Qfy7J22bZdl+11ILOKcX8vmcDI4w0NUcdXdHWRlQfkgSdvvFFH9+yJOj59/nwtefhhR+d6Zt26mCUpapcvj5mBPBwKaWDHDgUHBuStqVHN0qVFFeSWMv/aOrFsTRhNhYEzou3xKV1SpMKZeY2NJuNz4lvueQs6kPuc1Ex2+q4MpzWmo5Uzqqws7lr+mX5NjVW2Zrx09Fko9ucaAHIZbz0HCoO3tjZmoDuRDOJUSxqVeTy6bOVKx9crRJl8bXW7bA1QbAh0F7H6epPpF87Mu+QS8/jLL5OlB+SDeDWTJed1sp3WmKacUXSZfE1N9cYEAKBw5XMzNACGm40B3QyaF7NMvbbSVBhIDYHuIkdmHpC/xmcRDw+bRodhU6dKZWXOM4idZIdLJnDe2GjqQqer6WE+y9RrKjcmAACx5GszNACGmxnEToLmg4GAdre1KdjXJ+9YALeiqiqptReyTLy20lQYSE3OBboty5oq6ceSpsisb6tt2z7Lsi6Q1CHpUkkHJH3Utu3XIxy/VNImSaWStti2fXeGlg4AGTc+i3jvXunoUWn6dNPwMJEM4qYmKVZiSHm5VFoq/fmfS5deKlmWdPKkCaSuWWMC6jQhzJxEbkw4yegHgHRjjw8AiXErg7iiqkrzP/tZ/ebLXz5nrK61VQeffvqsgPrRPXt0oLOTetBZQgY+kJqca0ZpWZYlyWPb9nHLssokdUtaLekjkl6zbftuy7LukHS+bdtrJxxbKmmfpMWSXpb0S0l/Zdv23ljXLNZmNQAQ1t0tXXedCV5HM3mydOpU5DEa12ZWvOaX42uCcwMCxY5mlLkh03t89vcAYERrbCgpagA8jAa2mUdTYcCZaHv8kmwsJhbbOD72ZdnYhy1puaQHxx5/UNKHIxx+jaT9tm3/1rbtU5K+M3YcACCKYFBatix2kFuKHuSWzjQ9RGaEy9Z4vSaoLUkVFdKUKdJNN0mbNpkbDwS5AeQK9vgAkHmxGhtKihnkluLXi4b7wmVrIqGpMBBfzpUukd7I2tglabakr9i2/XPLst5i2/ZhSbJt+7BlWZEKRs2UdHDc1y9L+tO0LxiuCAZNoIy6v0BmdXRIx4/HnxcLTQ8zb2LzS5oIA8h16d7jW5b1SUmflKSamhq3l48EUO8XyA1dq1aldDz1oLODpsJA8nIy0G3b9oikBZZlnSfpMcuy3u7wUCvS6SJOZCPsqmSD1OHjdu6UHn3UvNV+cJC6v0Am3Xdf6ueg6WF20FAYQD5J9x7ftu0HJD0gmdIlya6z2KUapJ5YJoF6v0B2DAYC+sOLL6Z0DupBZw9NhYHk5GSgO8y27T9YltUlaamk31uWddFYpsdFkgIRDnlZUvW4ry+RdCjKudkIpyjVIHV3t9TYKI2MmOPGCzdYa2yk7i+QTvv2Sc8/n/p5aHoIAHAqnXt8pCbVIHWsMgk969erevFiMhKBDIlV59mpBbG61QNADsq5Gt2WZc0Yy/KQZVnlkq6T9KKkTkk3j027WdJ3Ixz+S0lzLMuqtSxrsqQbx46Dy7q7TSO01aul9nbpxIkzwepQyATBGxujl0MIjweD5wa5x6PuL5Beb3tb6ueorDQ3trghBQCIhj1+7osXpB46ciTuOeLV86XeL5B75n3iExEfpx40gHyUc4FuSRdJ2mlZ1m9kNrVP27b9PUl3S1psWVavTMf1uyXJsqyLLcvaJkm2bZ+W9A+SnpT0gqT/sG3bhVxFjOdGkLqjw4zHQ91fIDdNniyVlUlr10qHD1NiCAAQF3v8HOdGkDoYp54v9X6B3DN05IhWdHWpdvlyTZ8/X7XLl2tFVxelhgDkpZwrXWLb9m8kXR3h8aOSPhDh8UOSGsd9vU3StnSusdi5EaTu7T1TniQW6v4C6RMMJnfcHXfQ+BAAkBj2+LnPjSC1t7ZWR/fsiTpOvV8gMwYDkapARXasr4960AAKRi5mdCPHuRGknjPHjMdD3V8gfZIpC1RVJd11l2mASJAbAIDC4Y0ThHYSpI5Xz5d6v0BmJFImiBtQAAoJgW4kzI0gdVOTGY/G45G8Xur+AunU25v4Mb//vfvrQO4IBqUtW6TbbpNuvllqaTFfJ5v9DwDIH24EqSuqqlTX2hpxjHq/QObEe4fGeNyAKg6DgYCeWbdOT954o55Zty6hrH8gn+Rc6RLkvqYmac2a6OMejwlixwpSh4PYjY2mDEooJFVUSCMj0g03SA0N5jq2bYIsvb0mwN7UZI4FkLo5c6TSUvN754Rtp3c9yK7ubvOaPDxsGgyHTZ1qXvO3baMWOwAUsnCQOlJDykSC1HObm1W9eLF2t7XpWF+fptXWakFLyznHD4dC6t++XcH+fnlnzdKsZctU5iSbBkBc8coIhXEDqjjsa28/67X96J49OtDZqbrWVmqxo+BYNpEL1dXV2T09PdleRl4JB0RiBamdZGIfP27KJ+zff27N34nXGB9AJ9gCpC4YlGbOdJatGwzy7opC5uRnweuVDh3i5wD5x7KsXbZt12V7Hcgs9vfJGzpyJG6QOlWBXbvUtWqV7NFRjQwNqbS8XFZJiRZt3qyqhQtdvRZQjAYDAT3e0BB1/JIPfEB/8n/+D0HuIhDvZ2FFVxc/B8hL0fb4BLrFRjhZsYLUqYoVdCHYArgnfENpaEg6ffrssYoKk/HNzaXCFy5XEqv/gscjbdpk6rMD+YRAd3Fif5+7hkMhPdbQoNMR/tGZ5PFoxc6dZHYDLpiYxRtGFm9xeWbdOh3o7Iw6Xrt8OY1IkZei7fEpXYKkVVamL+DR0WEyuSMZHTXjBFuA1NXXmxtH4ZtWl1xiHn/5ZfdvYCF3OWkyHAqZnxEAAFLRv3277CgbfXt0VAM7duiylSszvCqg8DgtI4TCFq9e+7EE6rkD+YBAN3JSrKALwRbAXem8aZWsYNAE33t7pepq89jBg9TqT5dwk+F4Gd2zZ2duTQCAwhTs79fI0FDEsZGhIQUHBjK8IqBwlc+YkXPZuoOBgHa3tSnY1yfvWPC9oqoq28sqWPHqtU+rrc3gaoD0I9CNnBQr6EKwBShsE+vzj+fx0BgxHeI1GZZMj4SmpsysBwBQuLyzZqm0vDxisLu0vFzemposrApAJtAUMfMWtLTELF2yoKUlg6sB0q8k2wsAImlqMkGVSAi2AIUrGDRB7mAw8o2uUOjMnOPHM7++bAoGTS3ttWvNZydNRJ3yes3NA69Xmjr17LGpU8+MU8YGAJCqWcuWyYqy0bdKSlSzdGmGVwQgEwYDgYg1wyWpZ/16DR05kuEV5Y7hUEj7t27Vr+65R/u3btVwvJqCCaioqlJda2vEsbrWVkrZoOCQ0Y2cFA6qjM/q9HhMkJtgC1C4YtXnH6/YavVPzHJPR2b7+Hrte/dKR49K06dL8+ZRqx0A4J4yj0eLNm9W16pVskdHNTI0pNLyclklJVq0eTONKIECtbutLe54rpVZyYTArl3nvB4+u3GjFm3erKqFC125BvXaUUwIdCNnTWySR2M8oPA5aYooFVet/vFZ7mHh71Fjo3mddOt1MRfrtQMACk/VwoVasXOnBnbsUHBgQN6aGtUsXUqQGyhgNEU813AopK5Vq3R63H+AwmWdulat0oqdO117XczFeu1AOhDoRk4j6AIUFydNEaXiqtUfK8u92DLbAQCFo8zj0WUrV2Z7GQAyhKaI5+rfvl12lI2+PTqqgR07eJ0EEkSNbgBAzohVn3+8YqrVHyvLvZgy2wEAAJC/4jU9LMamiMH+/oiNeSWT2R0cGMjwioD8R6AbAJAzxjdFjPQuPY+n+BojhrPcIymmzHYAAADkL5oinss7a5ZKy8sjjpWWl8tbU5PhFQH5z7JtO9tryLq6ujq7p6cn28sAAIw5fvxMff5LLjGPvfxycdbqDwalmTPPrtEd5vW6W6MbKESWZe2ybbsu2+tAZrG/B4DcNHTkCE0RxwyHQnqsoeGsGt1hkzweV2t0A4Um2h6fGt0AgJxDff4zwhnsjY2mJncoZDK5S0qKK7MdAAAA+Y+miGeUeTxatHmzulatkj06qpGhIZWWl8sqKdGizZsJcgNJINANAECOq683mdvhLPdizGwHAAAACk3VwoVasXOnBnbsUHBgQN6aGtUsXUqQG0gSgW4AAPIAWe4AAABA4SnzeHTZypXZXgZQEGhGCQAAAAAAAADIawS6AQAAAAAAAAB5jUA3AAAAAAAAACCvEegGAAAAAAAAAOQ1At0AAAAAAAAAgLw2KdsLAIpRMCh1dEi9vdKcOVJTk+T1ZntVAAAAAJI1HAqpf/t2Bfv75Z01S7OWLVOZx5PtZQEAUDQIdKNohIPLzz0nvf66dMEF0pVXZj7I3N0tNTZKo6NSKCR5PNKaNdK2bVJ9febWAQAAAOS7wUBAu9vaFOzrk7e2VgtaWlRRVZXxdQR27VLXqlWyR0c1MjSk0vJyPbtxoxZt3qyqhQszvh4AAIqRZdt2tteQdXV1dXZPT0+2l4E0CgeXh4elEyfOPD51qlRWlrkgczAozZxpPk/k9UqHDkmVlelfB4DCwrtEgOgsy9pl23ZdtteBzGJ/Xxz2tberZ/36cx6va23V3ObmjK1jOBTSYw0NOh0KnTM2yePRip07yewGkBTeKQJEFm2PT0Y3Cl4waILckYLLJ06Yj8bGzASZOzpMJncko6Nm/JZb0rsGALnBrXeZ8C4RAEAxGgwEIga5Jaln/XpVL16s8hkzMrKW/u3bZUfZ5NujoxrYsUOXrVyZkbUAyD633mnCO0WAxBHoRsGLFVwOy1SQubfXBKIiCYWk/fvTe30A2RUObu/cKW3dal57Tp8+Mz51amJB6kg38sKvMe9/v7RhgznnwYNkegMACsvutra449du2JCRtQT7+zUyNBRxbGRoSMGBgYysA0B2DQYC+vFnP6vX9ux547Gje/boQGdnwu80GQ6F1LVq1VnvFAm/zvzg4x/X+ZdfrmmXXZa1ck1AriLQjYIXK7gclqkg85w5Jtsy0no8Hmn27PSvAUB2hDOvR0akwcHIcxJ9l0msG3nDw9Ltt5/5mkxvAEAhCfb1xRw/FmfcTd5Zs1RaXh4x2F1aXi5vTU3G1gIgO6KVUgpL9J0mMd8pMjKi155/Xq89/3xSQXSgkJVkewFAuoWDy7FkKsjc1CSVRPmtKykx4wAKz/jM62hB7vHC7zKJx8mNvLBQ6Mw6jh93dgwAALnKW1sbc3xanHE3zVq2TFaUTb5VUqKapUszthYAmRerlNJ48d6JMl6sd4pM1LN+vYaOHHF8bqCQEehGwYsVXA7LVJDZ6zXZlF7vmeC7x3PmcRpRAoXJSQml8Zy+y8TJjbyJnAbRAQDIZQtaWlIad1OZx6NFmzdrksej0vJySSaTe9LY4zSOAwqb0wB2Iu80Cb9TxO01AIWO0iUoeOEgcmOjeSv/iRNnxqZOlcrKMhtkrq83JQk6Okwga/ZsE2QnyA0UrkQyryXn7zJpajLlSBJBPwAAQCGoqKpSXWtrxCzKutbWjDWiDKtauFArdu7UwI4dCg4MyFtTo5qlSwlyA0UgXimlsETeaTJr2TI9u3Gj4/mZLNcE5DIC3SgK44PLe/dKR49K06dL8+a5G2QON5p77jnp9delCy6Qrrzy3AZwlZXpb3wJIHfEqs8fidN3mYy/kXfypHTqVPxj6AcAACgUc5ubVb14sXa3telYX5+m1dZqQUuL60HuwUBAu9vaFOzrk3fsGpGav5V5PLps5UpXrw0g93lra3V0XAPKaBJ5p0n4nSJdq1bJHh2NW8Ykk+WagFxm2bad7TVkXV1dnd3T05PtZSDPhRvNxcoapwEcUJyCQWnmTPM5nspKafv2xF4vjh+XHnzQZHfHC3Z7vc4aXYaFb+D19krV1eaxgwdN8H7iTTwgF1mWtcu27bpsrwOZxf4ebonWYI7mbwDCBgMBPd7QEHNOsq8Zw6GQBnbs0GsvvKDehx+OOm9FV1dSN/mc3sgDck20PT6BbrERRuqcBLESDS4BKCzhm2Gjo+dmdk+aJFmWCVR/4QvJv07EuobHYzLFE7np5vb5gGwg0F2c2N/DDfGCV8kGlgAUnmg3xaZfdZXe+3//ryuvFW7feONGHvIZge4Y2AgjVVu2SLfdFrssgccjbdpEyRKgmB0/fqY+/yWXmMdefvlMrX7bPpM9nWzGdLxrJJLJ7SQLnZt4yHUEuosT+3u44Zl163SgszPqeO3y5bp2w4YMrghALhs6ciRmKaXhUEj927cr2N8v76xZmrVsWcJ1/ONdwylu5CHfRdvjU6MbcIGTRnM0gANg2+ZjdFSaMuXsQPbE7GmPx2R4J5ox7VYPgI4Os5Z4RkfNXG7iAQAKTbwGczR/AxA2HArpdz/6kaZOn64Z73znOUHswK5dZ9XbLi0v17MbN2rR5s2qWrjQ8XXKZ8xw5Qbb7ra2uOPcyEM+ItANuMBJozkawAHFLVYg+x3vMGPjs6fDryeNjdnJmHZyA0/iJh4AoHDFazBH8zcAUvwg9nAopK5Vq3R63OY63Fyya9Uqrdi5M+HM7lRxIw+FqiTbCwAKQVOTqVUbS0mJmQeg+ASDZwLZ4f1tKHTm8QcfjJ49Hc6YDp9nyxZp7Vrp/vvNx9q15jEnjS4TEb6BFw838QAAhWpBS0tK4wAK3/ggdjh4PTI0pNNjj4fLldhRNvv26KgGduyQZMqJPLNunZ688UY9s26dBgOBtK3bG+dGHTfykK/I6AZc4PWarMzGRml4WDpx4szY1KlSWZkZp4YtUJxilQEZHZWeeCJ69nQ4YzpeY8hkypzE0tRkzhkPN/EAAIWqoqpKda2tUZu1Ub8WgJMgdrC//40g+EQjQ0MKDgyc0xjy6J49OtDZmbbGkAtaWmL2IOBGHvIVgW7AJfX1prxAR4e0d6909Kg0fbo0b15iDeAAFJ5YZUDCj0crfzRpkjQwIC1bZhpNxjqHm2VOxt/AixZcLynhJh4AoLDNbW5W9eLFrjR/A1B4nASxvbNmyZoyRfbJk+fMKS0v1+Tzzot4Q02SetavV/Xixa6/5nAjD4WKQDfgIreawAFwTzBobkD19ppyHOMbQGZKrDr+Ho/0oQ9J//3fkY89fVraulU6dSr+ddxuDDn+Bt7+/dIll5jHX37ZlCvhJh4AoBi41fwNgDsGAwHtbmtTsK9P3rGbTxVVVVlZi3fWLJWWl0cMdpeWl8tbU6NTwWDEILckWSUlem3v3pjXSFdjSG7koRAR6AYAFKRgULrzTum++yTLkk6eTE+JDydilQEpKZFuvtms8dZbI89xEuSW0tMYkht4CAvfNHruOen116ULLpCuvDI7N48AAEBxynSJj3hmLVumZzdujDhmlZRoRl2dvtfYGPX4d911l174t3+LeY10NobkRh7CcukGUioIdAMACk53d+RSH+ko8eFEpDIg40t/2LZ0xx2pX4fGkEiXcI34SH0osnHzCAAAFJ/BQCDjJT7iKfN4tGjzZnWtWiV7dFQjQ0MqLS+XVVKiRZs367mvfS3m8b/7wQ/kra3V0T17os6hMSTSLdduIKWiJNsLAADATcGgCchFq2ctnSnxkUnhMiCbNpmg9qZN5uv6+tjNKhNBY0ikQ/h3Khg8O8gtma+d/M4BAACkandbW0rj6VK1cKFW7NypunXrNO8Tn1DdunVasXOnqhYuVDBONvaxvr64jR9pDIl0incDaejIkQyvKDVkdANR5EJdXwCJcxI0TkeJDyeilQGJ1axyopKSc/9+NIZEOjn5nXK7PjwAuK1Q3pINFDMnQeNsKfN4dNnKlec8Hi9b+8Rrr+knn/2sLrjqKr0WYR6NIZFuTm4g5VN5GwLdwDjh4PbOndKjj5rA0eBg9ur6Akick6BxrpX4iNWscqLRUam01Hzceqs0ZQqNIZFeTn6nsnXzCACcKKS3ZAPFLB9LfCxoadGBzs6o46GXX1bo5Zff+Hr6VVdJlkVjSGRMLt9ASgaBbmBMuP7oyIgJbo+Xrbq+ABLnJGicayU+YjWrjGRkxHx87WtSIMBrEtLLye9Urt08AoCwXKzpCyA58YLGuVjio6KqSnWtrVFfhyY6umePVnR18bqEjMnHG0ixUKMb0Nn1RycGucfLRl1fAIlpajKB7GgqK3OvxEe4WaXXawKGTg0NSQ8+mL51AVL83ykp924eAUBYrtb0BZC4cNA4klwu8TG3uVkrurpUu3y5ps+fL88ll8Scz+sSMqnQasQT6AbkvBEcb80Gcl+koPHkyVJZmbR2rXT4cG6WIBrfrLKuzvlxTzyRvjUB0tm/U1Onnj02deqZ8Vy6eQQAYYX2lmyg2E0MGtcuX64VXV05X4aofMYMXbthg5Y8/LAmx8ls+cNLL2VoVUD+3kCKhtIlgJw3guOt2UB+CAeNOzrMzal8qWEdblb54x9LPT3ZXg1wxvjfqb17paNHpenTpXnz8uN3C0DxKrS3ZAM4EzTOV6fiBB+GnXapB1wyt7lZ1YsXa3dbm4719eV1jXgC3YCcN4LjrdlA/ggHjbMt3OS2t9e81jQ1mQzYWC64wPn5r78+tfUBTuXK7xQAJCIfa/oCyF2DgYB2t7Up2Ncn71gwsKKqKqFzTK6sVKzQQ1m8/ywALhoOhdS/fbuC/f2a8c53qq61VWWJ1NPMMQS6AcVvBOfxmCA3b80G4EQ4uL1zp/Too+b1Y3DQvJasWWNeS2KVT7nySlMS4sSJ2NepqJBuvtndtQMTJXOzBgByRaxGcPn4lmwA2bOvvf2s15Kje/boQGen6lpbEyqd8qa5c/X6iy9GHT9vzpyU1gk4Fdi1S12rVskeHdXI0JBKy8v17MaNWrR5s6oWLsz28pJi2bad7TWcxbKsakkPSbpQ0qikB2zb3mRZVoekt41NO0/SH2zbXhDh+AOSgpJGJJ22bTtupdO6ujq7h/eIF73ubtOQcnTUZHZXVEgjI9INN0gNDbw1G4Az4deSkZHozW29XlMGItprSjAozZxpPkcyebL52L49N+uNo3BM/Ldx/I3ffPnZsyxrl5P9INIr03t89veYaOjIkYJ4SzaA7BgMBPR4Q0PU8RVdXY5fU9w8F5Cs4VBIjzU06HSE0gaTPB6t2LkzpzO7o+3xczGj+7Skz9m2/axlWV5JuyzLetq27TcKRliWdY+kP8Y4R4Nt26+me6EoLPla0xdA7ggGTVAwWoA6bHTUvNZEKwMRbu7HzTdkU6Sf5/A+uLEx9s0aIAL2+MiqfK/pCyC7dre1xR13+hrDO02QC/q3b5c9OhpxzB4d1cCOHbps5coMryp1ORfotm37sKTDY38OWpb1gqSZkvZKkmVZlqSPSnp/1haJgkX9UQCp6Ogwgel4QiFzQy0Wbr4h22L9PMe7WQNMxB4fAJDPgn19McePxRmfqJCa/yE/Bfv7NTI0FHFsZGhIwYGBDK/IHTkX6B7PsqxLJV0t6efjHn6PpN/btt0b5TBb0lOWZdmS/tW27QfSu0oAAIze3vhNbSVT/mH27PjzuPmGbIr18+zkZg0QDXt8AEC+8dbW6uiePVHHp9XWJnxO3mmCbPLOmqXS8vKIwe7S8nJ5a2qysKrUlWR7AdFYllUp6RFJt9m2fWzc0F9JejjGoe+2bfudkpZJutWyrPdGOf8nLcvqsSyr58iRI66tGwBQvObMMUHseEpKTHY2kMti/Tw7vVkDTJTOPT77ewBAuixoaUlpHMg1s5Ytk1USOSxslZSoZunSDK/IHTkZ6LYsq0xmA/xt27YfHff4JEkfkdQR7Vjbtg+NfQ5IekzSNVHmPWDbdp1t23UzeGsIAMAFTU0miB2Nx3Om/nYuliAJBqUtW6S1a6X77zcfa9eax+LVHUfhifXzfPq09Nxz/GwgMene47O/BwCkS7iudiT5Uld7MBDQM+vW6ckbb9Qz69ZpMBDI9pKQRWUejxZt3qxJHo9Kp041D1qWrEmTdO1dd+V0I8pYLNu2XTmR3++3JE2XVOHz+ZIu5DJWn+9BSa/Ztn3bhLGlktbZtv2+KMd6JJWM1f3zSHpa0j/btr0j1jXpyg4AxS0YNPWGe3tNFmtTkwlIJ6O7Oz+bSE5c93gejwl4bttmaoejeHR3S0uWSIODkcfz4WcjWkd2OJOve3z29wCAdBg6ciQv62rva2+P2vxybnNzFlaEXPHCN7+pX33xi+c8nus/G9H2+CkHuv1+/zslfUHSdZI8kmyfzzdp3Pj5ku6Wqav3WZ/PdyrOQusl/UTSHknhFkift217m2VZ35T0M9u2vzZu/sWStti23WhZ1ltlMjwkU3+83bbtc3+TJ2AjDADFKRiU7rxTuu8+ybKkkyfdCdwdP55fTSSDQWnmzPiZuV6vaZCZy38XuCsYlC6+2PxMx5LLPxsEupOT73t89vcAUNwGAwHtbmtTsK9P3rGAdEVVVbaXlRWDgYAeb2iIOr6iqysvgvVwXz7/bETb46dUusTv9/+NpJ9K+rCkSknW2McbfD7f65JqJf0/khbHO6dt2922bVu2bc+3bXvB2Me2sbGPjd8Ajz12yLbtxrE//9a27XeMfVzpJMgNAChO3d0mgLdxo3TqlAlySyabORg02c3xgnvRhJtI3nWX+ZyLwb/xOjpMJnc8o6NmLopHR4fkJCeCn43Cwh4fAJDP9rW36/GGBh3o7NTRPXt0oLNTjzc0aF97e7aXlhW729pSGkfhKsSfjaQD3X6//wpJ/yapTNKXJdVJejXK9IdkNsfLk70eAABucRLILqbAXW/vueVKIgmFTJY6igc/G8WHPT4AIJ8NBgIRS3RIUs/69RoqwmbFwb6+mOPH4oyjcBXiz0YqGd1rJE2W9BWfz3ebz+d7VtJIlLk/HPt8bQrXAwDAFR0dpm52LMUUuJszx5RsicfjMaVYUDyc/mxMmiQNDNDAtECwxwcA5K1CzFBNlbe2Nub4tDjjKFzxfjYmn39+3jUwTSXQ/X6Zmnz/Em+iz+c7JGlQUk0K1wMAIGXBoPS1r0VvrhdWTEHdpiZTlzyekhIzF8XD6c/G6dNSe7v0mc+YckCrVpnSQN3d6V8jXMceHwCQlwYDAR360Y9izsnHDNVULWhpSWkchSvec3/4Rz/KuxJAqQS6L5YU8vl8LzucPySpPIXrAQCQku5u6S1vkXbtij+3mIK6Xq9pvun1Rs7e9XjOzMn1euNw1/ifjalTnR93+rQpDXTddcnXukfWsMcHAOSdcF3uU3/8Y8x5xZi9XFFVpbrW1ohjda2tOdtsEOkX62cjmlwvAZRKoPukpMl+v9+KN9Hv95dLOk9S7FccAADSJBiUli2Thobiz62sLL6gbn29dOiQtGmTdMcdpgTF/febP2/aZMbq67O9SmRDfb300kvJHXvypPTAA+6uB2nHHh8AkFdi1eWeqFizl+c2N2tFV5dqly/X9PnzVbt8uVZ0dWluc3O2l4Ysm9vcrOoPfjChY3K5BNCkFI49IOkqSXMk7Yszt1FSqaS9KVwPAICkdXRIw8Px573rXdLTTxdXkDusslK65ZZsrwK56IknpNLS5I79xjekNWvcXQ/S6oDY4wMA8ojToFuxZy+Xz5ihazdsyPYykIMGDx9OaH7g2WfTtJLUpZLRvUOmy/rqWJP8fv90SRtlav09kcL1AABIWm+vyS6NpaxM+sQnijPIDcTS22satCYjXj185Bz2+ACAvBJ0UHd7TnMz2ctAFPGaUk4UOngwTStJXSoZ3W2SPi3p7/1+f0DSveMHx97KuELSekmzJL0q6WspXA8AgKTNmSNNmRI72F1aWjx1uYFEzJljarUnE+y+7DL314O0Yo8PAMgr3tpaHd2zJ/qE0lJdcPnlmVsQkGcWtLToQGdntpfhCsu27aQP9vv9H5K0VVKZpGGZDPFSSS9KequkyTIZISclfcjn8/0g1QWnQ11dnd3T05PtZQAA0igYlC6+OHZjvCeflMaXJwsGTcmT3l6puto8dvCgCfo1NZkGfUAxCAalmTPN50SVlUmnTrm/Jqcsy9pl23Zd9laQfwphj8/+HgCKx2AgoMcbGqKOl5SXa+WPfqSyCV3XBwMB7W5rU7CvT97aWi1oaVFFVVW6lwvkpH3t7Y5r3UvSiq6urJYCirbHTynQLUl+v/8aSfdLivYfiF9J+nufz/fLlC6URmyEAaA4dHdLS5acW0qhrEz63vfODnJ3d0uNjdLo6LlZrB6PVFJiGlbSoBHFItbvRDwpbjdTQqA7Ofm+x2d/DwDFJVqQrmTyZL1/yxZVLVzoaH5dayslTlC0ho4c0e62NvV997tx59YuX57Vmu9pC3SH+f3++ZLqJV0sk/HxiqT/9vl8Ob/DZCMMAMXj+HHpwQdNcz1Juv566eabz67L7TR71euVDh2ipjeKx/Hj5l0On/hEYscR6M5f+brHZ38PAMVn6MgRPfvFL+rVX/9ak8rL9dYPf1iz//IvI2Zyx8oAz3amKpBt7VdeGXfO9PnzteThhzOwmsjSHujOZ2yEAQDjbdki3XZb/KxVj0fatEm65ZaMLAvIGZaV2HwC3cg09vcAgGieWbcuZj3ibGeqAtnmJNCd7d+TaHv8kmwsBgCAXNbb66w0Qygk7d+f/vUA+Wzy5GyvAAAA4IxgX1/M8WNxxgGYBpa5iEA3AAATzJljsrXj8Xik2bPTvx4g11x8sfO5//RPaVsGAABAwry1tTHHp8UZBwpd2fTpMcdLKitztryPo9Ilfr//hy5dz/b5fB9w6Vyu4a2NAIDxqNENxHbokPkdiaesTHrttez+jlC6JLpC3uOzvwcAREONbiC2fPgdibbHn+Tw+EVxxsPR8okVG8dH0a0JXwMAkJO8XmnbNqmxURodPbeMiccjlZSYOQS5UWyCQfOz39Ag7dwZfV5ZmfS97/E7kuMWxRlnjw8AKDgVVVWqa21Vz/r154zVtbZmPYAHZNNwKKSDTz9t/sM7OnrOeElZmSZVVGRhZc44DXT7ozw+WdIqSedJ+p2kLkkvj43NlNk8XyLpdUlfk3QquWUCAJBZ9fUma7Wjw9ThvuQS8/jLL5tyJU1NBPBQPIJB87uwc6e0das0MmI+4rnhBhMUr69P/xqRFPb4AICiNLe5WdWLF2t3W5uO9fVpWm2tFrS0EORGUTq6d6+6W1o0FAhodHg4apBbkkaHh/ULv1/v3rgxw6t0xlHpkkj8fv8kSd+X9KeSPitpi8/nsyfMsSTdIun/SvqZpOt8Pp+D/xZlFm9tBAAAiKy7W1q2TBoachbcnijbJX4oXZKYQtnjs78HAACI7ydr1ujgk08mfFy2y5ekWrokkhZJ75F0q8/n+7dIE8Y2xVvGNsxfGTvmSylcEwAAABkQDEoPPijddltyAe6wU6dMNvgtt7i2NKQXe3wAAIACNxgI6Oc+nw7/+MdJHb+7rU3Xbtjg8qpSV5LCsTdJOi3pGw7mfkPSiKS/TuF6AAAAyIDubtNscs2a1ILcknTypLR3rzvrQkawxwcAAChg+9rb9XhDQ9JBbkn6w0svubgi96QS6L5M0nGfz3cy3sSxOcGxYwAAAJCjgkHTiDUYlIaH3Tnn0aPunAcZwR4fAACgQA0GAhEbsSZqOBRyYTXuSyXQfVrSeX6/f2a8iWNzzh87BgAAADmqoyNq75mkTZ/u7vmQVuzxAQAACtTutjZXzlPm9bpyHrelEugOd3dxUo8vPIeOMAAAADmst1dyO0Fj3jx3z4e0Yo8PAABQoIJ9fa6c57w5c1w5j9tSaUZ5r6QPSPqo3++vknSnpG6fzzcsSX6/v0xSvaRWSQ2S7LFjAAAAkKPmzJE8HveC3SUlUlOTO+dCRrDHBwAAKFDe2lod3bMn5fMsaGlxYTXus2zbTvpgv9//T5L+UWaDK5m3Lb469vUMmUC6NTZ2p8/n+8ekL5ZGdXV1dk8PiSgAAOSSYNCU0ejtNcHXpiYpR98hV1CCQdOIMhh053wrVkiPPurOuZJhWdYu27brsreC/FMIe3z29wAAAOcaDAT0eENDzDlWebnsoaGo45O8Xn30Zz9ze2kJibbHT6V0iXw+3z9J+rCkF2U2u2WSLpJ08difLUkvSPpILm6AAQBAbgkGpS1bpJtukqqqpNWrpY0bpdtuM8HX7u5sr7Dweb3Stm1SZaU755s7153zIHPY4wMAgHQYDAT0zLp1evLGG/XMunUaDASyvaSiU1FVpbrW1phzYgW5Jck7M24rl6xJKaN7PL/ff5WkOklVYw8FJPX4fL7U8+HTjIwPAACyr7tbamyURkakwcHIc7xe6dAh94KwiO7++6X/9b+kkydTO09lpXT4cPaeMzK6U5Ove3z29wAA5JZ97e3qWb/+nMfrWls1t7k5Cysqbj9padHBp55K6tgLrrpKS7/zHZdXlJhoe/xUanSfZWyzm9MbXgAAkJuCQRPkjlcuY3TUlDO55ZbMrKuYHTyYepBbkmyb5yyfsccHAACpGgwEIga5Jaln/XpVL16s8hkzMryq4jZ4+HDSx762Z4+GjhzJyecspdIlAAAAbujoMEHseEIhaf/+9K8HUnW1NGVK6ufhOQMAAChuu9vaUhqH+youuijm+OTzzos5nqvPGYFuAACQdb29JiAaj8cjzZ6d/vUUu+5uad06dzK6JWnaNHfOAwAAgPwT7OuLOX4szjjcta+9PW7ZEs+FF8Yc/0Nvr5tLck3SpUv8fv9vkzjM9vl8lyV7TQAAUJjmzDFB7HjB7pISqakpM2sqVuEyMsePu3fOb33LBM6R+9jjAwAAt3lra3V0T/RKaNNqazO4muIWq4xMWF1rqw58//sx57y+d6/2tbfnXH31VGp0X+pwni3TmT38ZwAAgLM0NUlr1kQf93hMkHvbNhpRppvTMjKJ2LvXBM557vLCpQ7nsccHAACOLGhp0YHOzpjjyIx4JUeqlyzR3OZm/epLX4p7rlysr55KoPvjccbfJOlPJX1E0nFJ/yhpMIXrAQCAAuX1miB2Y6MJsoZCUkWFNDIi3XCD1NBgguEEStPPaRmZRNGQMm+wxwcAAK6qqKpSXWtrxEziutbWnAqUFrp4ZWTCTSpHHNYw3N3Wpms3bEh5XW5JOtDt8/kedDLP7/dfIelpSTdJWpTs9QAAQGGrr5cOHTIB0f37TS1ugtuZ57SMTKJoSJkf2OMDAIB0mNvcrOrFi7W7rU3H+vo0rbZWC1paCHJnmNtlZHKtvnram1H6fL4XJN0q6c8k3Zbu6wEAgPxVWWmyfu+6y3wmyJ15TU2mTIybLIsmooWGPT4AAEhU+YwZunbDBi15+GFdu2EDQe4siFcmJtEyMrlWXz3tge4xT0g6JelvMnQ9AAAAJCFcRsbrNeVj3DBpEk1ECxR7fAAAgDwSLiMTyfzPflblM2ZoMBBwfL5cq6+ekUC3z+c7LemkJLqxAwAA5Lj6emnrVlMvfVIqHV3G+P1k5xci9vgAAAD5Z25zs+avXn3O47/58pe1r71djzc0ODpP9ZIlOZeVn5FAt9/vnyvJK2k4E9cDAABAcoJB6f77pT//c+nECen06dTP+c//LB0/nvp5kFvY4wMAAOSX4VBIe//93/WbTZsijkdqGBrNwSef1NCRI24tzRVpD3T7/f6Zkr4pyZbUk+7rAQAAIDnd3dLMmdLnPiedOuXeeUdGTJNRFA72+AAAAPklsGuXHmto0O777nPtnLvb2lw7lxuSfjOq3+//9zhTpkqqlvQnksrGHvtSstcDAABA+gSDUmOj+ey24WFp/373zwv3sccHAAAoPMOhkLpWrdLpUMjV8x7r63P1fKlKperix2QyOCwHc49Lus3n8z2ZwvUAAACQJh0dpiZ3usyenb5zw1UfE3t8AACAgtK/fbvsNGz2p9XWun7OVKQS6H5IZhMczWlJr0v6taT/8vl8x1K4FgAAANKot1dymuDh8TifG3b99YmvCVnBHh8AAKDABPv7NTI05Gjuiq4uPbZokaO5C1paUliV+5IOdPt8vo+5uA4AAABk0Zw5sQPYkydLliXdcIPU0CB94hOJnb+9XVqzJvV1Ir3Y4wMAABQe76xZKi0vjxvsrmttVfmMGY7PGxwYSGh+uqW9GSUAAAByX1OTVBJlZ1hWJt17r/Tqq9K3viXdckvi5//GN1JbHwAAAIDkzFq2TFa0zb5ladb112tFV5fmNjcndN6uVas07HLd71QkHej2+/3/7vf7701g/ka/3//1ZK8HAACA9PF6pW3bzGePxzxWUSFNmSL95V+az/a4ghbveEdi5x8cdG+tSB/2+AAAAIWnzOPRos2bNcnjUWl5uSSpZOpUlUyerFmNjXrLNddoUkXFG/MnXXCBo/Pao6Ma2LEjLWtORioZ3R+TdGMC8/9y7BgAAADkoPp66dAhadMmk+F96pRpUNneLt16q/TmN0tr10rBoAmKJ+Jd70rPmuG6j4k9PgAAQMGpWrhQK3buVN26dbrkuus0euqURoeH1f/EE9p11116rKFBgV27JEkfeuQRR+ccGRpScGAgnctOSCZLlzjp3A4AAIAsqqyUqquljg7p9GlpeNg8fuqU+di4UTrvPOmLX5SWLnV+3nvuSctykX3s8QEAAPJEmcejU8eO6eXvf99ktIy9ZXPkxAmdDoX0w099SsOhkCqqqnT+FVfEPV9pebm8NTXpXrZjmQx0v1kSb1oFAAAxBYPSli0mc3jLFvM1MicYlP7iL2LPGR2V7rtPeuopZ+f86EelCy9MeWnITezxAQAA8sRgIKBffelLUcdHh4b0S79fkjRy6lTc81klJapJJPslzSal+wJ+v/9Nkj4hqULSnnRfDwAA5KdgULrzThNAtSzp5ElTK3rNGlMmo74+8fN1dEi9vdKcOaYUh9eblqUXlI4OaWTE2dzRUWfz3vrW5NeD3MQeHwAAODUYCGh3W5uCfX3y1tZqQUuLKqqqsnaeYra7rS3unANPPKGrb79dp0+ciD3RsrRo82aVhRv85ADHgW6/3++T9I8THn6L3+93+F8h2ZK2Or0eAAAoHt3d0rJl0vHjZz8ebuDd2GhqR1dWxj/XoUPS3/2d9PTTUkmJKb+RSsC82PT2mu+ZWzweafZs984Hd7HHBwAA6bSvvV0969e/8fXRPXt0oLNTda2tmtvcnPHzFLtgX5+jeU//zd/o/Le9TYO/+13UOdUf/KCqFi50a2muSLR0iTXuw57wdayPYUn/LuluV1YNAAAKRjBoAtkTg9zjjY6aTON4vvpVaeZM6cknzTHhgG0o5Ow6MNnvpaXuna+kxGTTI6exxwcAAK4bDATOCk6P17N+vYaOHMnoeSB5a2sdzTt+8KB+98MfxpxTt26dG0tyVSKlS74pqWvsz5akH0p6TdLKGMeMSjomaZ/P5xtKYn0AAKDAdXTEL4ERCkn798eec+iQdOutsecMD5vr3XJLYmssJk1N0j/8g/PyJbFMnmy+1//v/0v5mBz2TbHHBwAAaRCvTMbutjZdu2FD3PP86otfjDn+7Be/qHdv3JjQ2orVgpYWHejsTPk881evVvmMGS6syF2OA90+n69fUn/4a7/fPyDp9z6f70fpWBgAACgOvb1nSpRE46T8xYoV8a914oS0d6/ztRUjr1fq7JSWLEn9XKdOmZrrYZ/9rGlgSfmY3MEeHwAApEu8MhnHHJbROPLrX8ccfzXOOM6oqKrS/M9+Vr/58pcdH3P+FVfo9RdeOOux32zapN9s2qS6f/5nzV0ZKz8is5JuRunz+S51cR0AAKBIzZljAtmxgt3xyl8cOiT94hfOrvfKK4mtrxh98IPSdddJ3/++u+cdGpIWLZL+8Adn9daReezxAQCAW7y1tTq6J3rP6mkOy2jEZVnunKdIvP1Tn9IL3/ymho8dczR/YpB7vJ5//Ef9pq1NN3R3u7W8lCRaozvtLMuqtixrp2VZL1iW9bxlWavHHv8ny7J+Z1nW7rGPxijHL7Us6yXLsvZblnVHZlcPAAAS1dRkAtnRVFaaJpKxAqP/6385vx4l/Jx58MH0nHdkRHLwDlUUGPb4AAAUnwUtLSmNh8ULyJ764x8drwnG+7/+ddfOder11/U/3/2ua+dLhaOMbr/fXzP2x2Gfz3d4wmMJ8fl8A3GmnJb0Odu2n7Usyytpl2VZT4+Ntdm2/aVoB1qWVSrpK5IWS3pZ0i8ty+q0bZs3KQMAkKO8XhPIbmw0tbpDIVPb2balNWukL3whfvbvT3/q/HqTkn4/W3G5+GLpnnukz33O/XPfdx/B7lzAHh8AAKRTRVWV6lpbIzaSrGttdVzj+fRQ7JYg8cZxrunz5umi971Ph3/kTrW6n7e26rLly105Vyqc/lcvXDTnRUlXTngsEXa8a9q2fVjS4bE/By3LekHSTIfnv0bSftu2fytJlmV9R9JySWyCAQDIYfX1pvxIR4dpOjl7tsn0dlrewuNxfq3rr09ujcVo2jSpokIaHHT3vPxfJGewxwcAAGk1t7lZ1YsXa3dbm4719Wlaba0WtLQk1MiwdOpUnT5+POr4pKlT3Vhq0Znypje5dzLbdu9cKXAa6LYmfJ74Z6cSOsayrEslXS3p55LeLekfLMv6W0k9Mhkhr084ZKakg+O+flnSn0Y59yclfVKSamqSSlwBAAAuqqyUbrkluWM/9jHp9tvjzysvl26+OblrFKPeXveD3JLE/0VyRkHt8dnfAwCQm8pnzNC1Kbyd721//dd6/mtfizmOxMVrFpqPnNborh37uC7CY4l+OGJZVqWkRyTdZtv2MUmbJV0maYFMNsg9kQ6L8FjEWwq2bT9g23adbdt1MxK4iwQAAHLPpz4lTZkSfbyszATSn3qKJoiJCDcKddv73+/+OZGUgtrjs78HAKAwzfu7v5NVWhpxzCot1RUf/3iGV5TfhkMh7d+6VcPpyGjJMkcZ3T6fr9/JY26xLKtMZgP8bdu2H5Uk27Z/P2783yR9L8KhL0uqHvf1JZIOpWudAAAgN3i90ve/Ly1bJp04IZ0+LZWWmnrcN9wgNTQkVgql2AWDpozMc8+Z5pFuu/xy98+JxLHHBwAA+aDM49EHvvEN7fzUpzRy4oQpk2FZKp06VQ3/+q8qS0dmRoEK7NqlrlWrZI+OasTFeoKlFRWunSsVOdeOybIsS9LXJb1g2/a94x6/aKy2nyStkPRchMN/KWmOZVm1kn4n6UZJzWleMgAAyAH19dLhw9HrfAeD0pYtphzHnDlmzOvN7ppzUXf32Y1B3S4zMmWKNG+eu+dE7mOPDwAAUlG1cKE+8qMfaWDHDgUHBuStqVHN0qUEuRMwHAqpa9UqnQ6FXD+3q/W+U5BzgW6ZOn1/I2mPZVm7xx77vKS/sixrgczbFA9I+pQkWZZ1saQttm032rZ92rKsf5D0pKRSSf9u2/bzmV0+AADIlol1vsPB7Z07pUcflUpKTM1pj0das0bats0EyGEEgybIHQyeeezECfev09Tk/jmR89jjAwCAlJR5PLps5co3vh4MBPTLO+9UsK9P3rEmlxVVVVlcYW7r375d9uhoWs49453vTMt5E5V0oNvv9783wUNOSPqDpP/x+XxR3wRr23a3Itfh2xZl/iFJjeO+3hZtLgAAKB7hzOSRkXMbKoaTGBobpUOHKGkS1tFhMrnT6YYb+H7nMvb4AAAgH+xrb1fP+vVvfH10zx4d6OxUXWur5jbzxq9Igv39rpYrGe+dt9+elvMmKpWM7i5FafQYx0m/3/99SRt9Pl93CtcHAACIKFJmciSjoya4Oz4LvJj19p65CZAOU6eaeunIaV1ijw8AAHLYYCBwVpB7vJ7161W9eLHKaUx9Du+sWSotL3c92F3+lrfkzPe7JMXjrSQ+pkr6kKQuv9+/OsXrAwAAnMNpZnIoZOp5w5gzx9TQdqKiwsydNcv5+U+ckKqr489D1rHHBwAAOWt3W1tK48Vq1rJlGj19OvJgaWnkx0vih46Hfv977WtvT2Fl7kk60O3z+Uok/YWk1yW9KOkWSZfJbHKnjv35FkkvSHpNZuN7gaQlkn44du17/H5/bhRxAQAABcNpZrLHY5pWwnjzm6WTJ+PPmzxZ2rhRevVVqb8/sWvccIN0/Hhy60P6sccHAAC5LtjXF3P8WJzxYvXKz34me3g48uDIiJY98ohqly/X9PnzVbt8ud73wAOO6xr2rF+voSNHXFxtclKp0X21pP+U1C3pQz6fb+J/i/ok9fn9/m9LekLSVkl/6vP5npb0tN/v/55M3b1bZTbLAAAArpgzxwSx4wW7S0pojBgWDEp/9VfO5paVmTIkl12W+HUoF5Pb2OMDAIBc562t1dE9e6KOT6utzeBq8sNwKKTuNWtiznnxoYd07YYNb3zdfuWVCV1jd1vbWcdnQyqlS9ZJmizp1ggb4Df4fL5Tkv5BJgNk3fihsc+JNrwBAACIqakp9rvsPB7J65W2baMxYlhHh2nc6UQoJH3iE1IgkPh1KBeT89jjAwCAnLagpSWl8WLUv3277Dib/fGZ8IkGuSceny2pBLrrJR3z+Xz74k30+XwvSfqjpEXjHtsl06X94hTWAAAAcI5wENvrNUFt6UxN6ZtukjZtkg4dkurrs7vOXNLbK0V7J+NElpX8dSgXk/PY4wMAgJxWUVWlutbWiGN1ra050xgxlwT7+yU7dr/xcCb89z760aSuUZkDzXiSLl0i6XxJtt/vt3w+X8zvlN/vL9GZun7jDUly2PIIAADAufp6E8zu6DAZxLNnm0xvMrgjmzPH3AwYHIw/N84eOSbKxeQ89vgAACDnzW1uVvXixdrd1qZjfX2aVlurBS0tBLmj8M6apdKpUzVy4kTUOeFM+GPPP5/UNS6YNy+p49yUSqD7kKRLJf25pM44cz8ks9l9I4fd7/dPldlIJ9jCCAAAwJnKSmpBO9XUJMV7l2dFhWnIHgwmfx3KxeQ89vgAACAvlM+YkfWa0Pli1rJl2vUv/xJ1vGTyZE2qqEjpGidffz2l492QSumSTkmWpH/z+/1/Fm2S3++/VtIDkmydvVkOF3v5bQprAAAAgAu8XunTn44+XlYm/eVfmiz5VPzmN6kdj7Rjjw8AAFBgyjwezb3xxqjjVmmpBnbsSPr8peXl8tbUJH28W1LJ6L5T0kclXSTpx36//8eSfiSTBWLL1OVbJNOIpkTS4bFjwv567PMPUlgDAAAAMmB4WLroIpONvXq1qXOejFtvlT7yEenCC91dH1zDHh8AAKDIjAwNKTgwIEmaduWVCZcvsUdGVLN0aTqWlpCkM7p9Pt9RSQ2Snhs7zyJJ/yjpa5L+Vabj+qKxseclvX/smLDvSloh6evJrgEAAADumTPnTPPOiaZMkS65xPz5vvtSu84dd6R2PNKHPT4AAEBh8s6apdLy8ohj4zOyP/Qf/5HwuUdPndJpJ81+0syyU+kmJMnv90+S1CTpLyW9U1K46vsRSc9K2iqpw+fzDad0oTSqq6uze3p6sr0MAACArAoGpYsvlo4fjzw+ebL0X/8lffCDZu60acld513vkn760+TXmQjLsnbZtl2XmasVjnzf47O/BwAAONtwKKRH6us1eurUOWNWSYneeccdeuuHP6yyscyX9iuvPGdeLLXLl2esZnq0PX4qpUskST6f77Skb499AAAAIE/9+tdShH3vG06dkpYskR57TPrwh5O/ztvelvyxyAz2+AAAAIXllZ/9LGKQW5Ls0VHtvvde/XrTJi3avFlVCxcmfP5jfX3xJ6VZyoFuAAAA5L9g0GRqxwp0h61YIT35ZPLXuvvu5I8FAAAAkJjhUEg/ue22mHNGTpyQJP3g7/5O58+dm/A1ptXWJrM0VyVdoxsAAACF48EHpaEh5/OXLEnuOvPmRa8DDgAAAMB9vf/xH9LoqKO59unTem3v3oSvsaClJeFj3OZKRrff779W0nxJF0gqizXX5/P9sxvXBAAAgHu+973MXKe3V5o5U9q2Taqvz8w1kRz2+AAAAIXht48/nvZrHHz6ac1tbk77dWJJKdDt9/sXy3Rfn5XAYWyCAQAAitTwsPlobJQOHZIqK7O9IkzEHh8AAKCwjCTy1s0k9axfr+rFi1U+Y0b8yWmSdKDb7/dfI+l7OpPd0SfpkKTTLqwLAAAAGfShD6VWdztRp09LHR3SLbdk7pqIjz0+AABA4Zn+jnco9Lvfpf06u9vadO2GDWm/TjSpZHT/H5kN8IuSPurz+Z5zZ0kAAADItJtvlv73/06sTncqhoak/fszcy0khD0+AABAgXnn7bdrYNu2tF/nWF9f2q8RSyrNKK+VZEv6GzbAAAAA+c3rlZ56ypQSmeRKF5f4Zs/OzHWQEPb4AAAABaaiqkp1ra1pv87kN70p7deIJZVAd4WkQZ/Pt8utxQAAACB76uulffukj35Uestb0n+9pqb0XwMJY48PAABQgOY2N2vpI4+o4qKLEjqupCxmT/KzvPqb3yS6LFelEujuT/F4AAAA5JDubpNl3d4u/f736b3WVVfRiDJHsccHAAAoQPva27Vj5UoNHj7s+Jh3bdyoksmTHc8fDgaTWZprUtnEPiJpqt/vf69biwEAAIgkGJS2bJHWrjWfs7x/KkjBoLRsmTQ4mJnrXXddZq6DhLHHBwAAGTUcCmn/1q361T33aP/WrRoOhbK9pIIzGAioZ/36hI/72f/+36qsqdEkj0eyrPgHjI5m9flLJdB9t6TfSvqK3++f7tJ6AAAAztLdLc2cKd12m7Rxo/k8c6Z5HO7p6JCGhzN3vX/7N+n48cxdD46xxwcAABkT2LVLjzU0aNfdd+uFf/937br7bj3W0KDALqqouWl3W1vSx/7hhRf0nk2bdNG73x1/cmmpBnbsSPpaqUql1dA7Zbqyf0XS836//wFJP5cUM8fK5/P9OIVrAgCAIhIMSo2NZ2dwhxMEGhulQ4cof+GW556TTp7M3PVs2wTXb7klc9eEI+zxAQBARgyHQupatUqnx2UAjwwNSZK6Vq3Sip07VebxZGt5BeWPL72U0vE/aWnRaSdvqx0ZUXBgIKVrpSKVQHeXTEf2MCetO+0UrwkAAIpIR4c0Ohp5bHSUQKlburulf/3XzF4zFJL278/sNeFIl9jjAwCADOjfvl12lM2+PTqqgR07dNnKlRleVeHZ196u11MMdDsKckuSZclbU5PStVKR6obUQXGWlOYDAIAi1tt7JoN7IgKl7ghnzZ84kdnrejym8SVyEnt8AACQdsH+/jcyuCcaGRrKamZwoUi2NneyrNJS1SxdmrHrTZR0oNvn89GNHQAApNWcOSYgGinYTaDUHbGy5tOppERqasr8dREbe3wAAJAp3lmzVFpeHjHYXVpentXM4EKRSm3u8UorKjTioGt9/b33ZrXcDBtZAACQs5qaTEA0EgKl7oiVNZ8ulZXStm3UVwcAAChms5YtkxVls2+VlGQ1M7hQBPv6XDnP5TffHHfO1bffruoPfMCV6yWLQDcAAMhZXq8JiHq9JoNbMp/Dj6crUBoMSlu2SGvXms9OS9Llo3DWfKoaG6Unnzz7uRpv8mTzsXatdPiwVF+f+jUBAACQv8o8Hi3avFmTPB6VlpdLMpnck8YepxFl6ry1tSmfo2TyZM37+MdV1xq5dcv0q67Siq4uXfGxj6V8rVRZtm3Hn+WA3+/3ynRpr5JpSHNE0rM+ny/n/2tYV1dn9/T0ZHsZAAAgiuPHTYmN/ftNuZKmJveD3MGgucbOndKjj5qM8cFBE7QtKTGB9UIMzgaD0syZqQXzL7hAOnrU/Hn8c3XJJeaxl19O3/MWj2VZu2zbrsvsVQtHvu7x2d8DAJA/hkMhDezYoeDAgLw1NapZutT1IPdgIKDdbW0K9vXJW1urBS0tqqiqcvUauWgwENDjDQ3JHVxaqpLJk/X+f/1XVS1cKEkaOnJEu9vadKyvT9PGvo/lM2a4uGJnou3xUw50+/3+qyStl7RM52aIj0p6QtL/8fl8e1K6UBqxEQYAoLh1d5uM5JERE9yOxOuVDh0qzHIb4b//iRPS8HDix99/v3Trre6vyw0EupOT73t89vcAACBsX3t7xIaMda2tmtvcnIUVZVa0v38sb3nXu3RpY2Nabjq4IdoeP6XSJX6//yOSfi7pekmlMh3Xx3+USvpzSb/w+/0rUrkWAABAOgSDJsgbDEYPckumYWNHR+bWlUn19SaI39YmTUqiVfl3v+v+mpA97PEBAEChGAwEogZ5e9av19CRIxleUebNbW7Wiq4uzbr+esmyHB3z+5//PGeD3LEkHej2+/21kr4taaqkfkmfljRHUvnYY3PGHjsgaYqkb48dAwAAkDM6OkwQO55QyJTjKFSVlSYre+fOxGt2/+AHJisc+Y89PgAAKCS729pSGi8U5TNm6N0bN+q6Bx9U6dSp8Q+wbQ3s2JH+hbksiZydN9wus7n9qaQlPp/v+ITx/5H0P36///+T9JSkd0n6nKR/SOGaAAAArurtNUHseDweU2e60NXXS6+8Il1zjfTCC86OGR01WfGFWtqlyLDHBwAABSPY1xdz/Fic8UJTtXChPvLjH2vrtdfKHhmJObd/xw5dtnJlhlbmjlRKl1wn05Dm7yNsgN/g8/lCkv5e5m2OH0zhegAAAK6bM8dZBnNJiWmmWOiCQek735He/vbEjivk0i5Fhj0+AAAoGN7a2G88mxZnvNAMBgL65Z13xv2+SNIrzzyTd6VdUgl0XyIp6KQBzdicY2PHAAAA5IymJhPEjsbjMY0ot20r/Gzl7m5p5kxp9WrpP/8zsWMLvbRLEWGPDwAACsaClpaUxgvJvvZ2Pd7QoAOdnTrmcOOeb6VdUgl0D0sqczLR7/dbkiaPHQMAAJAzwkFsr/dMZndFhTRlinTTTdKmTaYkR319dteZbk6bckZTLKVdigB7fAAAUDAqqqpU19oacayutVXlM2ZkeEXZEaspZyz5VtollUD3fklT/X7/Egdzl8g0ryHPBwAA5Jz6ehPM3rRJuuMO6ctfll59VfrWt6Rbbin8TG7JeVPOaEZHi6O0SxFgjw8AAArK3OZmrejqUu3y5Zo+f75qly/Xiq4uzW1uzvbSMibZzOx8K+2SSjPK70q6WtK/+f3+JT6fL2K7Ir/fP0/SAzK1/h5P4XoAAABpU1lpgtrFymlTzmgsy721IKvY4wMAgIJTPmOGrt2wIdvLyJp4TTmjufxv/9bllaRXKoHu+yT9PzI1+X7l9/v/U9IPJP1OZsNbLekDkm6QeUvjy2PHAAAAIMfMmSOVlkpxmq9HFW5GWcw3CwrEfWKPDwAAUFC8tbU6uiduC5ZzvPjQQ3l1gyDp0iU+n++YpKWSDshscpslfV3SDklPStoi6a/GxvokLfP5fMEU1wsAAIA0qKlJPsgtSSdOSHv3urceZAd7fAAAgMKTbAmSYqrRLZ/P97yk+ZLWSdotaVSSNfYxOvbYWknvGJsLAACAHBMMSitXpn6eo0dTPweyjz0+AABA4RgMBPSbTZuSOtZz0UUurya9UildIkny+XzHJf2LpH/x+/1lki4YG3rN5/MNS5Lf73+T3+9/VpLt8/kWpnpNAAAAuKejQzp1KvXzTJ+e+jmQG9jjAwAAFIZkG1FKpm5dPkk50D3e2Kb391Gus0D59/0BAAAoeL29qQe6p06V5s1zZz3ILezxAQAA8leyjSglafDwYRdXkn4plS4BAABAfgsGpVdeMY0oU1FWJjU1ubMmAAAAAKkbDAR04vXXkz4+2dre2eJqRjcAAADyR3e31NhomlAm24jS45FKSqRt26TKSnfXBwAAACA5+9rb1bN+fUrnWNDS4tJqMoOMbgAAgCIUDJogdzAoDQ46O6aszHyuqJCmTJFuuknatEk6dEiqr0/fWgEAAAA4NxgIOA5yX/S+90V8vK61VeUzZri5rLQjoxsAAKAIdXRIo6OJHTM8LC1ebEqUNDWRwQ0AAADkokQaUB7+0Y80f/VqBQ8c0LG+Pk2rrdWClpa8C3JLBLoBAACKUm+vFAolftzTT0sPPUSQGwAAAMhViTag/M2mTVrR1ZWXwe3xKF0CAABQhObMMfW1k3HHHe6uBQAAAIB7vEk0kUwkCzxXEegGAAAoQk1NpolkMl56yd21AAAAAHBPMk0kjyWYBZ6LHJcu8fv9I+lcCAAAADLH65W2bZOWLpVOnJBGEtjpve1t6VsXMos9PgAAQGE5unevultaZE2aJPv0acfHTUsiCzzXJFKj20rbKsZfxLKqJT0k6UJJo5IesG17k2VZX5T055JOSfofSR+3bfsPEY4/ICkoaUTSadu26zKxbgAAgHxx6JC0bp30ox8lV6f77rvdXxOyhj0+AABAgfjJmjU6+OSTSR2bTBZ4rkkk0O1P2yrOdlrS52zbftayLK+kXZZlPS3paUnrbNs+bVnWv0haJ2ltlHM02Lb9aobWCwAAkDe++lXp1luTP/4rX5EuvNC99SDr2OMDAAAUgKN79yYd5K5rbc37RpRSAoFun8+XkU2wbduHJR0e+3PQsqwXJM20bfupcdN+JumGTKwHAADAiWBQ6uiQenul6mrz2MGDpuljU5MpFZJthw4lF+QuK5NKS6Urr5Suu879dSF72OMDAADENxgIaHdbm4J9ffLW1mpBS4sqqqqyvayzdCeTkV1Somvvvlu111/v/oKywLJtO9triMqyrEsl/VjS223bPjbu8f+S1GHb9rciHNMn6XVJtqR/tW37gSjn/qSkT0pSTU3Nwv7+fvf/AgAAoCh0d0uNjdLo6LmlQDwe0/Rx2zapvj476wtrbpYefjj187S0SPfem/p5MsGyrF2Uucgt6drjs78HAADpsK+9XT3r15/zeF1rq+Y2N2dhRZF9Z+FCjZ44kdSx511xhRq3bnV5RekTbY9fko3FOGFZVqWkRyTdNmED3Crz1sdvRzn03bZtv1PSMkm3Wpb13kiTbNt+wLbtOtu262YUQGo+AADIjmDQBLmDwcj1rkOhM3OOH8/8+sK6u6XvfMedc7W1Sf/zP+6cC8UlnXt89vcAAMBtg4FAxCC3JPWsX6+hI0cyvKLI9rW3Jx3klqQ/vPCCDv/0py6uKDtyMtBtWVaZzAb427ZtPzru8ZslfUjSTXaUVHTbtg+NfQ5IekzSNelfMQAAKFYdHSaTO57RUTM3Gw4dkhYvltx8I9/f/q1750JxYI8PAADyze62tpTGMyFWMD4RP/nsZ11YTXYl0owyIyzLsiR9XdILtm3fO+7xpTKNad5n2/ZglGM9kkrG6v55JH1Q0j9nYNkAAKBI9fZGzuSeKBSS9u9P/3rCwjXDd+6U/uM/pNOn3T3/gQPung+FjT0+AADIR8G+vpjjx+KMp8v4muEnXn/dlXOeHoy4FcsrORfolvRuSX8jaY9lWbvHHvu8pC9LmiLpabNP1s9s2/57y7IulrTFtu1GSW+R9NjY+CRJ7bZt78jw+gEAQBGZM8fU4Y4X7PZ4pNmzM7OmcM3wkREpXfvVSy9Nz3lRsNjjAwCAvOOtrdXRPXuijk+rrc3gaoxoNcNTNamiwvVzZlpON6PMlLq6OrunpyfbywAAAHkoGJRmzjSfY/F6TQmRysrcWE+q9u+XLrssvddwA80oixP7ewAA4IbBQECPNzREHV/R1aXyDPYGibeeVDRs2aKLrr02Led2W941owQAAMgHXq+0bZv57PGcO+7xnJmT7iC3ZMqVDA2l9xotLfkR5AYAAABSUVFVpbrW1ohjda2tGQ1yS+mrCX7eFVfkTZA7llwsXQIAAJBX6utNtnZHh8l0vuQS8/jLL5tyJU1NmQlyS9LPfuZuPe7586XSUun3vzflSh56iCA3AAAAisfc5mZVL16s3W1tOtbXp2m1tVrQ0pLxILckvRajjIpTb77mGv3hued0enBQkyoq9J4vf7kggtwSgW4AAABXVFZKt9yS7VWY5pNu+uQnpVtvdfecAAAAQD4pnzFD127YkO1lKPi738WeYFmafP75OvXaa1GnvPqLX2S85EqmULoEAACggMRripmo737X3fMBAAAASI49MhJ7QkmJJk2eHPc86SqBkm0EugEAAArIW9/q7vniJY0AAAAAyIzJ06bFHJ8ybZoGjxyJe55jfX1uLSmnEOgGAAAoIN/8ZrZXAAAAACAd3rNpU8zxP9u4UYqX9S1pWm2tW0vKKQS6AQAACsjcue7W1J45071zAQAAAEjeWxYuVNU110Qcq7rmGoUOHXJ0ngUtLW4uK2cQ6AYAACgAwaC0ZYt0003S178uTZly9vh73iO9613S29+e2HmXL3dvjQAAAAASNxgI6Jl16/TkjTeq4sILVX///Zpy/vlSaammnH++Fn/rW7ruG99QsL8/7rnOu+KKgmxEKUmTsr0AAAAApKa7W2psNO9SHByMPGf3bunQIek735FWr44+b7zycunmm11dKgAAAIAE7GtvV8/69W98fXTPHh3o7FRda6vmNjefNdc7a5ZKp07VyIkTUc/XsHlz2taabQS6AQAAEhAMSh0dUm+vVF1tHjt4UJozR2pqkrzezF5PMkHuYDD2eUZHzXmamqQ1a2LPnTzZfGzfLlVWpv53AAAAAPLBYCCg3W1tCvb1yVtbqwUtLaqoqsra9QYDgbOC3OP1rF+v6sWLz8rOnrVsmZ7duDHq9a6+/faCzeaWCHQDAAA4Fs6cHh2VQqGzxzweE0Detk2qr8/c9VatMuPxhELS/v0mEL9t29nnLSuTSkqkT3/alDyZPdsExAlyAwAAoFgkkjmdqevtbmuLeY7dbW26dsOGN74u83i0aPNmda1aJXtkxGR2W5as0lLV33uvqj/wAdf/HrmEQDcAAIADwWDszOlwILqx0ZQISTVI7PR6bW3S8HD883k8JoAtmUD8oUMmw3v/fgLbAAAAKG6JZk5n6np/3Lcv5nmO9fWd81jVwoVasXOnBnbsUHBgQN6aGtUsXaoyj8eVtecyAt0AAAAOdHQ4y5wOlwi55ZbMXM9JkFsyGdvhUieSCWqnukYAAACgECSaOZ2J602aNk2vv/hizHnTamsjPl7m8eiylSuTXl++ItANAADgQG/vueVDIgmXCMnU9ZyorDTlSsjYBgAAAM4VjJAZPd4f3djgJ3C9gaee0sjQUNzzLGhpcWtJBaEk2wsAAADIB3PmmPIf8UydeqZESCauF09ZmXT33WfXDQ8GpS1bpLVrzed4jSwBAACAQuaNkhkd9vpLLymwa1fGruckyF3X2lrQjSWTQaAbAADAgaYmU/4jnhMnpOuvz9z14hkell5++Uxw+6abpKoqafVqaeNG6bbbpJkzTeNLAAAAoBjFy4y2T59W16pVGnbpLZepZmKff/nlZzXIHAwE9My6dXryxhv1zLp1GgwEUl1iXiLQDQAA4IDXa8p/eL3S5MnR55WWmkzpRLOkJ2ZZS2eul2pmt22bYPbq1VJ7uwnGDw6asVDoTOPL48dTuw4AAACQjyqqqlTX2hpzjj06qoEdO5I6/3AopP1bt+pX99yj/Vu3qszjiXu9WM5729ve+PO+9nY93tCgA52dOrpnjw50durxhgbta29P+vz5yrJtO9tryLq6ujq7p6cn28sAAAB54Phx6S//Uoq1xy0rMyVMtm07u2RINN3dJtA8OmoCzx6Pyebetk1asMA0pty/X7rkEjP/M58xwWunKivjB7GnTpXuv7/wGlRalrXLtu26bK8DmcX+HgAAJOOXd96p3ocfjjo+7xOfSDgbO7Brl7pWrZI9OqqRoSGVlpfLKinRos2b5a2p0e62Nh3r69O02lotaGnRY4sWxT3niq4ulc+YocFAQI83NMSdV2ii7fFpRgkAAJCAykpp5UrpJz+J3ixyeNh8NDZKhw7FbgIZzqYenwEePm/4+InB5y1bpN27na33bW+T+vvjzztxQtq719k5AQAAgEJ0/uWXq7S8PGqN7MHDhxM633AopK5Vq3R63H8cwufuWrVKK3bu1LUbNpx1zKTKSp2OkaVSMmXKG8Hr3W1tMa//7Be/qHdv3JjQmvMZpUsAAAAS1NQkWVb8eaOjJhs7lo4OMy+R4+Odc7z3vtcEsZ04etT5eQEAAIBCM2vZMsV64+SBJ57Q0JEjjs/Xv3277Cib/WilUN731a/GPOcHvv71N/78x337Yl//iSdcbaKZ6wh0AwAAJMjrlT796fjzQiFTciSW3t7omeHRjp87V3LyjsmvfEU6eTL+vLDp053PBQAAAApNmcejqne+M+aceFnU4wX7+6Nmh48MDSk4MHDO429ZuFBV11wT8Ziqa67RjKuvfuPrE3/4Q9w1uNlEM9cR6AYAAEgTj0eaPTv6eDAo/fa3sbPDDx6M/Pi995og+J/9mXTxxVJdnbRihfSud0k33ywdPmyC8WVlztZaVibNm+dsLgAAAFCoho8dizl+rK/P0XkGAwEd+slPoo6XlpfLW1MTcey6b3xDi7/1LU05/3yptFRTzj9fi7/1LV33jW+8MWdfe7uGXnkl7jpSaaKZb6jRDQAAkIQ5c0wgO1ZyREmJKXMSSXe3dN118TOuv/1t6Utfki688Nyxyy6T/vu/ox/71a9K497ZGJNlRV8rAAAAUCy8tbU6umdP1PFptbVxz7GvvV0969fHnGOVlKhm6dKo4zOuvloru7sjjg0GAnHPHxYtc7wQkdENAACQhKYmE8iOprJS2rYtciPKYFBautR5WZE77kh8fYcOSbfe6nx+S0vsppkAAABAMVgQp0ZgvHEnQejS8nIt2rxZZR5PwuuTpJ677nI8N1bmeKEh0A0AAJAEr9cEsr1ek9ktSZMnmxIga9ea0iH19ZGP7ehIrHb2Sy8lvr5165zPLS2VvvCFxK8BAAAAFJqKqirVtbZGHKtrbVX5jBkxj3dSw3vKeeepauHCpNYnSYFf/MLx3HiZ44WE0iUAAABJqq83mdMdHaZe9uzZJtM7XmZ0b690+rTz67ztbYmv7fnnnc/9kz8hmxsAAAAIm9vcrOrFi7W7rU3H+vo0rbZWC1pa4ga5JSnooIb30KuvprQ+27bjziktL5dVUpJS5ni+IdANAACQgspK6ZZbEjvGwf74LHffndh8yWSXO/XXf534+QEAAIBCVj5jhq7dsCHh4yaff37cOZOmTk1mSW+onDlTr//xj1HHp775zXrHZz+rmqVLiybILVG6BAAAIOOefdb53EsvjdyIMp6rr3Y2r6JCuvnmxM8PAAAA4Fx/7O2NO2duipkmb54/P+Z49Qc/qMtWriyqILdEoBsAACDj/ud/nM993/uSu8bVV8fO6p40yWSjP/kkZUsAAAAAt5z8wx/izpn38Y+ndI3zr7hCJWVlEcdKysp0weWXp3T+fEWgGwAAIMMS2XcmU7YkGJROnJBGRyOPT5ok3Xdf7IaZAAAAABI3dfr0mOPlVVUpZVoPBgJ65ec/1+jISMTxksmTi6b55EQEugEAADLsrruczfvKVxIvW9LdLV18sfS5z0VveGlZ0kc+QiY3AAAA4Lb6traY44s2b0763Pva2/V4Q4MGtm07J6ultLxckzyeomo+ORGBbgAAgAy7+GITxI6kpsbUzD58WPr0pxM7bzAoLVkiHT8unToVfd7wsPTWt5qgOAAAAAD3TJ83T9VLlkQcq16yROcnWVZkMBBQz/r1Uccv/9jHtGLnTlUtXJjU+QsBgW4AAIAs+PSnTTD75puld73rTHC7v1/65jeTa0D54IPS4KCzuSdOSI2NJigOAAAAwD3vufdeLXvkEVVWV6u0vFyV1dVa9sgjes+99yZ9zl1x3hb6/ObN6vvud5M+fyGYlO0FAAAAFKsLLzRBbbd873uJzR8dlTo6pFtucW8NAAAAAKTzL79cf7Fjh2vn+/0vfhF3Ts/69apevFjlM2a4dt18QqAbAAAgw4JBE2Du7ZWqq81jBw9Kc+ZITU2S15uZdYRC0v79mbkWAAAAgPTb3damazdsyPYysoJANwAAQAaEg9s7d0qPPCLZ9rl1tD0eac0aads2qb4+8Wt86EPSk086n+/xSLNnJ34dAAAAAGcMBgLa3damYF+fKi66SLakocOH5a2t1YKWFlVUVaV8japrrtHLTz0Vd96xvr6Ur5WvCHQDAACkWXe3qYc9MhK7hnYoZD43NkqHDkmVlYld5+abpf/9v6WhIWfzS0pMBjkAAACA5Oxrbz+rSeTRPXvO+vOBzk7VtbZqbnNzStepW7fOUaB7Wm1tStfJZzSjBAAASKNg0ASug0HnjSLDtbMT5fVKTz1lMrVjmTrVzN22LfFgOgAAAABjMBA4K8gdTc/69Ro6ciSla1VUVamutTXuvAUtLSldJ58R6AYAAEijjg4TuE5EKrWz6+ulV16R1q6VJk82H5I0aZL5aGqS7r/fZIwnUx4FAAAAgLG7rS0tc6OZ29ysFV1dmn7VVRHH61pbi7YRpUTpEgAAgLTq7T1TksSpVGtnV1ZKd98tfeELJtC+f785X1MTGdwAAACAW4IJ1MN2q3Z2+YwZWvKd72joyBHtbmvTsb4+TRurBV7MQW6JQDcAAEBazZljAteJBLvdqp1dWSndckvq5wEAAABwLm9t7Vk1uWNxu3Z2+YwZunbDBlfPme8oXQIAAJBGTU0mcO2Ex+Ne7exgUNqyRbr1VunP/kyqqzPNKg8dSu28AAAAAIxE6mG7WTt7MBDQM+vW6ckbb9Qz69ZpMBBw7dz5jEA3AABAGoUD115v5CaR4TraN90kbdrkTu3sp56SqqqkT31K+upXpZ/+VNq1S3roIWnmTPMYAAAAgNQ4bRDpZu3s5x54QI83NOhAZ6eO7tmjA52deryhQfva2105fz6zbNvO9hqyrq6uzu7p6cn2MgAAQBYFg6aedW+vKTfS1GSC0245fvxMvexLLjGPvfyyO7Wzx6/91CnpvvviH3P4sHThhclfM19YlrXLtu26bK8DmcX+HgAAZNL4etmeiy6SLWnw8GFXamcPh0Lq375dwf5+HTtwQL/74Q+jzl3R1VUUdbqj7fEJdIuNMAAAxSocIN65U3r0UVNiZHDQZF6XlJhM7FSzq9Otu1tqbJRGRxOrA37zzdI3v5m2ZeUMAt3Fif09AAAYDAS0u61Nwb4+eccCzhVVVdleVkICu3apa9Uq2aOjGhkaiju/dvnyoqjbHW2PTzNKAABQlMIB4pERE9weLxwwbmw0pURSrZedLsGgWWMwmPixL73k/noAAACAXLCvvV0969e/8XW4xEdda6vmNjdncWXODYdC6lq1SqcTyGY51teXxhXlPmp0AwCAojM+QDwxyD3e6KjJ+E7nOrZskdauNZ8TDVh3dJg1JuNtb0vuOAAAACCXDQYCZwW5x+tZv15DR45keEXJ6d++XXaCm/1ptbVpWk1+IKMbAAAUHacB4lDI1NRO1fga2tXV5rGf/vTccilr1iRWLqW3N7FyJePdfXdyxwEAAAC5bHdbW9xxN8t7jK+hPeWCC/Ta3r0KHTyYcrmUYH+/o3Il4y1oaUnqWoWCQDcAACg6TgPEHo9pFpkKpzW0kymXMmeOWWOiwe6vfKU4GlECAACg+ATjlO9ws7xHrBraqZZL8c6apdLycsfB7vmrVxdFI8pYKF0CAACKTjhAHE9JidTUlPx1xpdIcRqMTqRcSlOTWWMspaVmzuWXmwaUhw9Ln/60s/MDAAAA+cYbp3yHW+U9xtfQjhWMTrZcyqxly2RZlqO581ev1ts/+cmEr1FoyOgGAABFp6nJlAmJxuMxweFt21JrRJlMDe145VLGl0GZM0f6p3+SPve5c+fddJMpkzJ7tvn75mpDTQAAAMBNC1padKCzM+a4GxKpoZ1MuZTXX3wx4vlLp0xR1Z/8iU4dO6ZpY+VRij2TO4xANwAAKDperwlijy8pUlEhjYxIN9wgNTS4ExxOpoZ2tHIpwaB0553SffdJliWdPGnWHK2ZZmen8xIoAAAAQKGoqKpSXWtrxIaUda2trgWFE6mhHatcymAgoN1tbQr29b1R17vM41HXqlUaOXHi3ANKS1V/770qc/IW1SJDoBsAABSl+noTCO7oMBnU6ch8TqaGdqRyKd3d0rJl0vHjZz8eLcgtnSmBcsstzq8NAAAAFIK5zc2qXrxYu9vadKyvLy2Zz4nU0I5WLmVfe/tZAflwXe9ZjY3Rs8VtWwM7duiylSuTWnchy7lAt2VZ1ZIeknShpFFJD9i2vcmyrAskdUi6VNIBSR+1bfv1CMcvlbRJUqmkLbZt352hpQMAgDxTWZneQHC8EinjRSuXEq7zPTHIHU+8EihAJrHHBwAAmVY+Y0bC5UISMWvZMj27caOjuZHKpQwGAhGzziWpf9u2qOcaGRpScGDA2SKLTC42ozwt6XO2bV8h6V2SbrUsa56kOyT9wLbtOZJ+MPb1WSzLKpX0FUnLJM2T9FdjxwIAAGRcMCj92Z+ZAHZp6bnjFRXSlCmmnvamTSbDvL7+7DnJ1PmWopdAAbKEPT4AACgog0eOqOLCC01dwRhNI6OVS9nd1hb7ApH+AyGptLxc3pqahNZaLHIuo9u27cOSDo/9OWhZ1guSZkpaLmnR2LQHJXVJWjvh8Gsk7bdt+7eSZFnWd8aO25v2hQMAAIzz1a9Kt94aeWzyZPP5M5+RvvCF2OVSkqnzLUUugQJkC3t8AABQSHb9y7/opYceOufxNy9cKM+FF+r4wYNxy6UEY9TtlhQ128UqKVHN0qUJr7kY5FygezzLsi6VdLWkn0t6y9gGWbZtH7YsqyrCITMlHRz39cuS/jTd6wQAABjv0KHoQW5JOnXKfP7qV02gOxandb7DjSmjlUABcgV7fAAAkM/+eOBAxCC3JL26a5eu3bZN3lmz4p7HW1uro3v2RB2/6N3v1pFf/Ur26KhGhoZUWl4uq6REizZvphFlFDkb6LYsq1LSI5Jus237mBXjLQDjD4vwmB3l/J+U9ElJqiHdHwAAuGjdOmfznDSMjFfnu7JSeuQR6eDB9DXVBNySzj0++3sAAJAJP/v852OO//Tzn9cHv/3tuOdZ0NKiA52dUcffdeedmlRRoYEdOxQcGJC3pkY1S5cS5I4hJwPdlmWVyWyAv23b9qNjD//esqyLxjI9LpIUiHDoy5Kqx319iaRDka5h2/YDkh6QpLq6uojBcAAAgGS8+KKzeU4aRnq9Jju7sdEExkMhU/rEtk0APF7pEyBXpHuPz/4eAABkQuh3v4s5fjzOeFhFVZXqWlsjNqQcX9f7spUrE19kkcq5QLdl0jq+LukF27bvHTfUKelmSXePff5uhMN/KWmOZVm1kn4n6UZJzeldMQAAwNkuv1z6xS/iz5s82VnDyPp6Uw6lo4OsbeQn9vgAAKBQeGbO1IlXX406XjlzpuNzzW1uVvXixdrd1qZjfX1x63ojtpwLdEt6t6S/kbTHsqzdY499Xmbz+x+WZd0iaUDSX0qSZVkXS9pi23ajbdunLcv6B0lPSiqV9O+2bT+f6b8AAAAobu97nxSlbN9ZTp2SFi1yds7KytglToAcxx4fAAAUhGmzZ+vor38ddfyKT3wiofOVz5ihazdsSHVZkGTZNu/qq6urs3t6erK9DAAAUACCQemCC6TTp53Nv/lm6ZvfTOuSipplWbts267L9jqQWezvAQBAOgwGAnq8oSHmnMrqav3Fjh0ZWlFxirbHL8nGYgAAAApVR4fzILck/fKX6VsLAAAAAPfsbmuLOyd0+HAGVoJICHQDAAC46LnnEpv/0kvS8ePpWQsAAAAA9/zxpZfizrFPn9bQkSMZWA0mItANAADgotdfT2y+bZsscAAAAAC57VQo5Giek8xvuI9ANwAAgIsuuCCx+aOj0v796VkLAAAAAPdMrqx0NO9YX1+aV4JICHQDAAC46LLLJMtK7JjZs9OzFgAAAADu8b71rY7mTautTfNKEAmBbgAAAJd0d0vr1plyJE6VlkpNTelbEwAAAIDUBXbt0qGuLkdzF7S0pHcxiIhANwAAgAuCQamxMfHGko88Ijl8ByQAAACALBgOhdS1apVODw7GnVsxc6bKZ8zIwKowEYFuAAAAF3R0mHrbibj1Vmn58vSsBwAAAIA7+rdvl+1wsz/vYx9L72IQFYFuAAAAF/T2Sg6bsL/hoYcSzwAHAAAAkFnB/n6NDA3FnVcyaZJqyWTJGgLdAAAAWTI6ajLBAQAAAOSuwVdecTRv9PRpR+VNkB4EugEAAFIUDEpf+Urix4VC0v797q8HAAAAgDsGAwH1b9vmeP7utrY0rgaxEOgGAABIUUeHNDyc+HEejzR7tvvrAQAAAOCORAPXx/r60rQSxEOgGwAAIEW9vdKpU4kfV1IiNTW5vx4AAAAA7ggmGLieVlubppUgnknZXgAAAEC+mzNHmjw5sWC3ZUnbtkmVlc7mB4Mmc7y3V6quNo8dPGiu3dQkeb2JrxsAAABAbN7aWh3ds8fx/AUtLQlfYzAQ0O62NgX7+uStrdWClhZVVFUlfJ5iR6AbAAAgRU1NUktLYoHuv/orqb7e2dzubqmx0TSvDIXOHvN4pDVrTNDc6fkAAAAAOLOgpUUHOjsdza1rbVX5jBkJnX9fe7t61q9/4+uje/boQGen6lpbNbe5OaFzFTtKlwAAAKTI65W2bzdZ3U7dc4+zecGgCXIHg+cGuSXzWHjO8ePOrw8AAAAgvoqqKtW1tsadd2F9fcKB6cFA4Kwg93g969dr6MiRhM5X7Ah0AwAAuKC+XjpwQJrk4P1y99wjXXihs/N2dJhM7nhGR81cAAAAAO665Lrr4s659s47Ez5vvEaXiTbCLHYEugEAAFxSWRk/q7uiQnrTm5yfs7c3cib3RKGQtH+/8/MCAAAAcCZewPmCq65KuGSJFL/R5bEEG2EWOwLdAAAALunoME0mYxkcTCwgPWeOqcMdj8cjzZ7t/LwAAAAAnIkXkLbi/ScgCm9tbczxaXHGcTYC3QAAAC5xkn2daEC6qUkqcbBjKykxcwEAAAC4K10B6QUtLSmN42wEugEAAFziJPs60YC01ytt22Y+Rzq3x3NmTmVlYusFAAAAEF+6AtKxGl3WtbYmVQ6lmDlolwQAAAAnmpqkNWuij1dWJheQrq+XDh0ypVH275cuucQ8/vLLJju8qYkgNwAAAJAu4YB0z/r154ylGpCe29ys6sWLtbutTcf6+jSttlYLWloIcifBsm0722vIurq6Orunp+f/b+/uo+066zqBf5+8tE1ubouUlJfSpsGmhYoanawuixFbFNpG5b0T6HIBTmehnS6EIKOWuGa71da3GYIzAi5UbHUEwsAUOpICDlikOA62Wi1TComkpSVAIlZ6e5OWJtnzxzmxIST3JTl7n3vu/XzWytrn7P3sc373t9rbX3959vMMOwwAYB647bZkw4bk4MHeMiYnnZQ0Ta8B/ku/pCHdtVLKHU3TrBt2HHRLfQ8AtGHfnj0a0nPAsWp8M7oBAAboyNnXZlwDAMD8sGzlylx0/fXDDoNj0OgGABiwFSuSq64adhQAAAALh80oAQAAAAAYaRrdAAAAAACMNI1uAAAAAABGmkY3AAAAAAAjTaMbAAAAAICRptENAAAAAMBI0+gGAAAAAGCkLRl2AAAAC8nERLJ1a7J9e7JmTbJxYzI+PuyoAAAARptGNwBAByYmkl/7teStb01KSR59NBkbS974xmTbtmT9+mFHCAAAzNbe3btz55Ytmdi5M+OrV2ftpk1ZfsYZww5rQdLoBgBo2W23JZdfnjz88Leen5zsHTdsSHbtSlas6D42AADg+Hzh3e/O7ddd96/vv37XXbn35puzbvPmnHfllUOMbGGyRjcAQIsmJnqN7COb3Ic7eLC3nAkAADAa9u7e/S1N7sPdft112bdnT8cRodENANCirVt7jeypTE4mO3Z0Ew8AAHDi7tyy5YSuM3ga3QAALdq+/fElSo5lbCw599xu4gEAAE7cxM6dU15/aJrrDJ5GNwBAi9as6TWyp7JoUbJxYzfxAAAAJ2589eopr586zXUGT6MbAKBFGzf2GtnHsmJFsm2bjSgBAGCUrN206YSuM3ga3QAALRof7zWyx8cfn9l90knJ0qXJL/xC8pWvJOvXDzdGAABgdpafcUbWbd581GvrNm/OspUrO46IJcMOAABgvlu/Ptm1q7cx5Y4dvfW4N240ixsAAEbZeVdembOe//zcuWVLHtq5M6euXp21mzZpcg+JRjcAQAdWrEiuumrYUQAAAIO0bOXKXHT99cMOg1i6BAAAAACAEafRDQAAAADASNPoBgAAAABgpGl0AwAAAAAw0jS6AQAAAAAYaUuGHQAAwEIyMZFs3Zps356sWZNs3JiMjw87KgAA4EQ9NjmZ+265JRP33ZfxVauy6vLLs3RsbNhhLRga3QAAHbnttmTDhuTgwWRyMlm+PHnd65KXvjS56KLemPvv1wAHAIBRs/uOO3Lr1VenOXgwB/bty+JTTsnf/OqvZvzss/PEZz87azdtyvIzzhh2mPNaaZpm2DEM3bp165rbb7992GEAAPPYxERy5pm943SWLk0WL04+9KHkBS9oP7b5rJRyR9M064YdB91S3wMAXXpscjI3XXJJ9k9OTjlu3ebNOe/KKzuKav46Vo1vjW4AgA5s3dqbyT0Tjz2WPPJIcumlycc+1m5cAADAibnvllvSzKDYv/2667Jvz54OIlqYNLoBADqwfXtvuZLZeuELk4cfHnw8AADAYEzcd18O7Ns3o7GffN3rWo5m4dLoBgDowJo1yfHsQ3PgQG82OAAAMDeNr1qVxcuWzWjsP991l1ndLZlzje5SyrtKKbtLKZ897NzWUsqd/T/3llLuPMa995ZS7uqPsygfADBnbNyYHM/WKPv3Jzt2DD4e6JIaHwCYz1Zdfvmsiv07t2xpMZqFa8mwAziKG5L8bpI/PnSiaZqNh16XUv5Lkm9Mcf8lTdP8U2vRAQAcp+NpdC9fnpx77uBjgY7dEDU+AECS5KGdO4cdwrw05xrdTdP8ZSnlnKNdK6WUJP82yfM6DQoA4ARt3ZosOo5n6RYv7s0Gh1GmxgcA5rP7brklKWXG409dvbrFaBauObd0yTR+KMnXmqbZfozrTZKPlVLuKKW8dqoPKqW8tpRyeynl9j3WxQEAWjbbzSiXL0/Gx5Nt25IVK9qLC+aAgdT46nsAYFhmsxllkqzdtKnFaBauOTejexqvTPKeKa7/YNM0u0opZyT581LKPU3T/OXRBjZN884k70ySdevWHceDxAAAg3Xyyb3jy1+eXHJJbya3JjcLwEBqfPU9ADAse7/61RmPXbd5c5atXNliNAvXyDS6SylLkrw0yb851pimaXb1j7tLKTcluTDJURvdAABdmZhI3v726ceVkuzcmTzlKe3HBHOBGh8AGHV7d+/Ofdu2HXvAokV54rOeldPOPTdrN23S5G7RKC1d8qNJ7mma5oGjXSyljJVSxg+9TvKCJJ892lgAgC5t3TqzjSgXL04+/OH244E5RI0PAIy0O7dsmfL6U5/znFz2vvflouuv1+Ru2ZxrdJdS3pPk/yQ5v5TyQCnlqv6lV+SIRxpLKU8rpRz6K5MnJ7mtlPL3ST6T5MNN03ykq7gBAI5lputzT04mO3a0Hw90TY0PAMxXEzt3Tnn9mw891FEkzLmlS5qmeeUxzr/mKOd2JdnQf/3FJN/banAAAMdhzZpkbGz6ZvfYWHLuud3EBF1S4wMA89X46tX5+l13HfP6qatXdxjNwjbnZnQDAMw3Gzcmi2ZQdS1a1BsLAACMhrWbNp3QdQZHoxsAoGXj48m2bb3j2Ni3Xz/ppGTp0uTqq2e2ljcAADA3LD/jjKzbvPmo19Zt3mxd7g5pdAMAdGD9+mTXruR3fie55prkwguTJz+5N4v7wIHkscd61572tOS224YdLQAAMFPnXXllXnLrrVn9ohfltO/8zixZvjyLTjkln7vxxnz97ruHHd6CodENANCRFSuSRx9N3va25DOfSb72teTgwV6jO+lde/jh5NJLe0cAAGA0LFu5MvsfeSTf+Md/zP69e3PwkUcy+cAD+egVV+RTb3zjsMNbEDS6AQA6smtXbzb3dPbuTW68sf14AACAwfj63Xfn/o9+9KjX7v/oR/PgPfd0HNHCo9ENANCRN71p5mM//OH24gAAAAbrU294wwld58RpdAMAdOC225L3vnfm4x99tL1YAACAwdl9xx3Z++UvTzlm7549HUWzcGl0AwC0bGIi2bAhaZqZ3zM52V48AADAYDw2OZlbr7562nGllA6iWdg0ugEAWrZ1a2/Tydl47LF2YgEAAAbnvltuSTODYr/R6G6dRjcAQMu2b5/9DO2lS9uJBQAAGJyJ++7LgX37ph3XmMnSOo1uAICWrVmTnHTS7O4ZG2snFgAAYHDGV61KFi+edtyik0/uIJqFTaMbAKBlGzfObn3uJFEHAwDA3Lfq8stntE7h4hk0wzkxGt0AAC0bH082bZrdPc98ZjuxAAAAg7N0bCxPec5zph13YP/+DqJZ2DS6AQA68PrXJ4tmUXn9/u8nDz/cXjwAAMBgfNdP//S0Yw5MTmbfnj0dRLNwaXQDALTsttuS88+f0RON/6ppkq1b24sJAAA4cV9497vz8Ve9akZj79yypeVoFjaNbgCAFk1MJBs2zH529uRksmNHOzEBAAAnbu/u3bn9uutmPP6hnTtbjAaNbgCAFm3dOruZ3IcsXZqce+7g4wEAAAZjtjO0T129uqVISDS6AQBatX17b3b2bDVNsnHj4OMBAAAGY2KWM7TXznaHemZFoxsAoEVr1iTLl8/+vpe9LFmxYvDxAAAAg7H8qU+d8djFy5Zl2cqVLUaDRjcAQIs2bpz90iUnn5w8//ntxAMAAAxGM4uxT3vuc1uLgx6NbgCAFk1MJE9+8uzuOekky5YAAMBcN3HvvTMeu+7aa9sLhCQa3QAArXn725Mzz0zuu29m45csScbHk23bLFsCAABz2R2/+Zv5xhe+MKOx6zZvtmxJB5YMOwAAgPlo167kmmtmPn7x4uStb01e/WpNbgAAmMu+ce+9+fwf//G048669NKsu/ZaTe6OaHQDALRgtk8mvvzls2uMAwAAw/HXb37ztGNO/+7vzg+95S0dRMMhli4BAGjBPffMbvwpp7QTBwAAMFiTX/7ytGP27tmTxyYnO4iGQzS6AQBa8Mxnzm78I4+0EwcAADBYY2eeOe2YfV/9am665JLsvuOODiIi0egGAGjFr//67MZ/8IPJww+3EgoAADBAP3D99TMat39yMrdefbWZ3R3R6AYAaMHTnpZceOHMxy9enGzd2l48AADAYJx2zjlZMsMd5JuDB/Olj3yk5YhINLoBAFpz0UUzH7t3b7JjR3uxAAAAgzM+g+VLkuTAvn2Z+NKXWo6GRKMbAKA1z372zDeZHBtLzj233XgAAIDBOO3882c0bvGyZRk/++yWoyHR6AYAaM3GjcnSpTMbu2hRbzwAADD3rd20aUbjyqJFOfuyy1qOhkSjGwCgNePjybZtveOxZnafcsrj42a4zB8AADBky884I+s2bz7m9cXLlmXJ2Fgufsc7snRsrMPIFq4lww4AAGA+W78+2bWrt9Hk3XcnX/96r7E9MZGcfnpywQW9mdya3AAAMFrOu/LKnPX85+fOLVvy0M6dWXHWWXniBRfk0QcfzPjZZ+fsyy7T5O6QRjcAQMtWrEiuumrYUQAAAIO2bOXKXHT99cMOg1i6BAAAAACAEafRDQAAAADASNPoBgAAAABgpGl0AwAAAAAw0jS6AQAAAAAYaRrdAAAAAACMNI1uAAAAAABGmkY3AAAAAAAjTaMbAAAAAICRptENAAAAAMBI0+gGAAAAAGCkaXQDAAAAADDSNLoBAAAAABhpGt0AAAAAAIw0jW4AAAAAAEaaRjcAAAAAACNNoxsAAAAAgJFWmqYZdgxDV0rZk+S+YccxjScl+adhB7FAyHV35Lo7ct0due6OXHdnlHO9qmmalcMOgm6NSH2fjPa/W6NGrrsl392R6+7IdXfkulujmO+j1vga3SOilHJ70zTrhh3HQiDX3ZHr7sh1d+S6O3LdHbmGdvh3qzty3S357o5cd0euuyPX3ZpP+bZ0CQAAAAAAI02jGwAAAACAkabRPTreOewAFhC57o5cd0euuyPX3ZHr7sg1tMO/W92R627Jd3fkujty3R257ta8ybc1ugEAAAAAGGlmdAMAAAAAMNI0ukdAKeWyUsrnSyk7Sim/OOx45pNSyrtKKbtLKZ897NwTSyl/XkrZ3j9+xzBjnA9KKWeVUv6ilPK5Usr/K6W8vn9ergeslHJKKeUzpZS/7+e67p+X65aUUhaXUv6ulPJn/fdy3YJSyr2llLtKKXeWUm7vn5PrFpRSnlBKeX8p5Z7+7+2L5BoGT43fHjV+d9T53VHnd0+d3x21fnfme62v0T3HlVIWJ3lbksuTXJDklaWUC4Yb1bxyQ5LLjjj3i0k+3jTNmiQf77/nxOxP8nNN0zwryQ8kuab/z7FcD96jSZ7XNM33Jlmb5LJSyg9Ertv0+iSfO+y9XLfnkqZp1jZNs67/Xq7b8TtJPtI0zTOTfG96/3zLNQyQGr91N0SN3xV1fnfU+d1T53dLrd+NeV3ra3TPfRcm2dE0zRebpvlmkvcmedGQY5o3mqb5yyT/fMTpFyW5sf/6xiQv7jKm+ahpmq80TfO3/dcT6f0iPTNyPXBNz8P9t0v7f5rIdStKKU9P8mNJ/uCw03LdHbkesFLKqUmem+QPk6Rpmm82TfMvkWsYNDV+i9T43VHnd0ed3y11/pwg3wO2EGp9je6578wk9x/2/oH+Odrz5KZpvpL0CrckZww5nnmllHJOku9L8n8j163oP2J3Z5LdSf68aRq5bs9bk/x8koOHnZPrdjRJPlZKuaOU8tr+ObkevGck2ZPkj/qP6v5BKWUscg2Dpsbvnt9jLVPnt0+d36m3Rp3fJbV+N+Z9ra/RPfeVo5xrOo8CBqCUsiLJB5K8oWmah4Ydz3zVNM2BpmnWJnl6kgtLKc8eckjzUinlx5PsbprmjmHHskD8YNM035/eY/7XlFKeO+yA5qklSb4/yTuapvm+JJMZ4UcXYQ5T4zOvqPO7oc7vhjp/KNT63Zj3tb5G99z3QJKzDnv/9CS7hhTLQvG1UspTk6R/3D3keOaFUsrS9IrfP22a5n/2T8t1i/qPIN2a3hqVcj14P5jkhaWUe9N75Px5pZT/HrluRdM0u/rH3UluSu+xf7kevAeSPNCfIZYk70+vGJZrGCw1fvf8HmuJOr976vzWqfM7ptbvzLyv9TW6576/SbKmlLK6lHJSklckuXnIMc13Nyd5df/1q5N8aIixzAullJLeGlCfa5rmLYddkusBK6WsLKU8of96WZIfTXJP5Hrgmqa5tmmapzdNc056v5s/0TTNT0auB66UMlZKGT/0OskLknw2cj1wTdN8Ncn9pZTz+6d+JMndkWsYNDV+9/wea4E6vzvq/O6o87ul1u/OQqj1S9N4Qm6uK6VsSG99qMVJ3tU0zXXDjWj+KKW8J8nFSZ6U5GtJqiQfTPK+JGcn+VKSK5qmOXIzG2ahlLI+yaeS3JXH1zh7c3rr98n1AJVSvie9zSMWp/eXme9rmuZXSimnR65bU0q5OMmbmqb5cbkevFLKM9Kb2ZH0Hrd7d9M018l1O0opa9PbeOmkJF9M8lPp/z6JXMPAqPHbo8bvjjq/O+r84VDnt0+t3635XutrdAMAAAAAMNIsXQIAAAAAwEjT6AYAAAAAYKRpdAMAAAAAMNI0ugEAAAAAGGka3QAAAAAAjLQlww4AgBNX1/UNSV6d5Maqql4z3GiOra7rpv/ykqqqbh1mLAAAMJep8QFmR6MbYBbquv7lJFWSVFVVOvi+i5NcnOTeqqpuOM7PeE2Sc5LcqvAEAIBvpcYHmB80ugHmtovTK7o/meSGKcZ9Jcnn+8cjvSbJD/df3zqwyAAAgONxcdT4AAOn0Q0wD1RVdW2Sa4cdBwAAMBhqfIDZsRklAAAAAAAjzYxugJbVdX1aksuT/ESS70lyZpLlSb6W5NNJ/mtVVX99xD3nJNl52KkfPmyTl0N+6tCafkfbqKa/bt8fHTa+quu6OuIzVldVde8R37e6qqp7j/Gz3Jtk1eHffcT170jy5iQv7f+cDya5LclvVFV1x9E+8yif8eL0HsW8MMmTkkwmuSvJu5P8YVVVj83kcwAAoC1qfDU+MPeY0Q3Qvk1J3pPkyiTPTrK0f/7sJK9M8ld1Xf/sEfccSK9Inuy/f6z//vA/+6b53n39cYeKxsmjfMaB4/qJjqJfSP9dkjcleUaSkl6x//L0fsYXTnP/irqu/1eSm5K8KMlT+z/DaUl+KMk7knyyX2gDAMAwqfHV+MAcY0Y3QPu+mmRLkq1JPl9V1b/UdV3S2yX99Ul+Nslb6rr+VFVVf5ckVVXdn+Qph+0A/1dVVV08my+tqmprkq11Xd+a3kY1/7mqql8exA90pLquFyf5H+nNBHkwyU8nuamqqv11XV+Q5PeS3DjNx/xJkh9PsiPJf0ryZ1VVTdR1fUqSF6SXw4uSvCvJS9r4OQAAYIbU+Gp8YI7R6AZoWVVVv3eUc016jxG+oa7rJUmu6f/59x2HNygvS7Ku//qKqqo+fuhCVVV313V9WZJ/SPKEo91c1/WPJXlxev/DcHFVVV8+7P5Hktxc1/XfJrknyYvrul5bVdWdLfwcAAAwLTW+Gh+YeyxdAjB8H+4f1w81ihPziv7x04cXwIdUVbU3yW9Ncf+h4v9PDi+Aj/iMB5L8Rf/tpccbKAAAdECNr8YHOmZGN0AH6rp+RpL/kOSSJN+ZZDzf/peNT+86rgE6NNPjE1OMmeraof8BeG1d16+aYtxp/eOqmQYGAABtUONPe02ND3RKoxugZXVdvyS9jWpOPuz0Q0keSdIkOSnJdyQZ6z66gTmjfzzqTI2+B452sq7rpentvJ70itzTjjbuCMtnHhoAAAyWGv9fqfGBOUOjG6BFdV2fnuSG9ArgTyT5lSSfqapq32FjfiTJ/x5KgIPXHMe1xYe9fkV/gx0AAJiT1PgzuqbGBzpnjW6Adm1Icmp6u5T/RFVVnzy8AO57SvdhfZv9h70+ZYpxx5qJsbt/nOrRzKNe629E843+2++e4n4AAJgL1PiPU+MDc4ZGN0C7zuofP9/frOVofnSK+w/2j+UEYpjJZzx42Ouzjjagruvzcowd1ZPc3j9eMsV3PG+Ka5/uH6+o69p/mwAAmMvU+I9T4wNzhl80AO06NIvhvLquv20WRV3Xa5NcOcX9D/WPTziBGKb9jKqqJpP8Y//ty44xbPMU33HoUcT1dV1ffOTFuq6XJfmPU9z/zv7xvGnGpa7rsbquT5pqDAAAtEiNHzU+MPdYoxvgONV1/aRphuxP8rH0Zls8Mcmf1nX9s1VVfblfxL04ye8mmUhy+jE+47P943fVdf2cqqr+6jhC/WySFyXZUNf1b1VVdazNZN6T5JeS/Lu6rv8hyR9VVbWvruuzklRJNibZm6NvEvOBJH+b5PuTfKCu69cm+WBVVQfqun5Wknfk8c1svk1VVR+q6/qmJC9J8ht1XZ+TZEtVVV9Ikn6+vi/JS5NclWRtjrHxDQAAHC81/rdQ4wMjRaMb4Pjtmeb631dVtbau699O8gvpFXAvrev6G+kVkkuT7Eyv8PzTY3zGrUk+n+T8JJ+u6/rBPD57401VVb1/BnHemOTnkpyb5Et1Xe9Jbzf4JFlfVdWhYvI3+zFekORtSf5bXdcPpTdL5LEkr0ryG0lWHfkFVVXtr+v6in68ZyV5f5JH67p+JL01/76Z5IokH5oizp9M8odJXpHkZ5L8TF3Xk/17T8u3PoU01YY4AABwvNT4fWp8YNRYugSgZVVV/WJ6BeRnkuxLr/jdkeT69GYw7Jri3v1JfiTJHyS5N8lYekXoqiQrZvj929NbV+/m9Ar30w/7jCWHjXs4yfokb0mvON+fXvH7gSQXVVX13mm+54vpzcI4dH9Jr9h+f5LnVFV18zT3762q6pX9WP8kyRfT++/UivQ2wvlEkp9PsmaKGSsAANA6Nb4aH5h7StP4CzMAAAAAAEaXGd0AAAAAAIw0jW4AAAAAAEaaRjcAAAAAACNNoxsAAAAAgJGm0Q0AAAAAwEjT6AYAAAAAYKRpdAMAAAAAMNI0ugEAAAAAGGka3QAAAAAAjDSNbgAAAAAARppGNwAAAAAAI+3/A/k1LXA1qCsmAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "    Now, plotting all the scatter plots side by side\n",
    "    in order to compare the interpolation techniques.\n",
    "\"\"\"\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.scatter(small_seagulls[const.LAT],\n",
    "            small_seagulls[const.LONG],\n",
    "            s=15, color='purple')\n",
    "plt.title('Original Trajectory', color='black', size=15)\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(25, 20))\n",
    "\n",
    "ax[0, 0].scatter(linear_ip_gulls[const.LAT],\n",
    "                 linear_ip_gulls[const.LONG],\n",
    "                 s=50, color='red')\n",
    "ax[0, 0].set_title('Linear Interpolation', color='black', size=40)\n",
    "ax[0, 1].scatter(cubic_ip_gulls[const.LAT],\n",
    "                 cubic_ip_gulls[const.LONG],\n",
    "                 s=50, color='orange')\n",
    "ax[0, 1].set_title('Cubic Interpolation', color='black', size=40)\n",
    "ax[1, 0].scatter(rw_ip_gulls[const.LAT],\n",
    "                 rw_ip_gulls[const.LONG],\n",
    "                 s=50, color='blue')\n",
    "ax[1, 0].set_title('Random-Walk Interpolation', color='black', size=40)\n",
    "ax[1, 1].scatter(kin_ip_gulls[const.LAT],\n",
    "                 kin_ip_gulls[const.LONG],\n",
    "                 s=50, color='brown')\n",
    "ax[1, 1].set_title('Kinematic Interpolation', color='black', size=40)\n",
    "\n",
    "for plot in enumerate(ax.flat):\n",
    "    plot[1].set_xlabel('Latitude', color='grey', size=25)\n",
    "    plot[1].set_ylabel('Longitude', color='grey', size=25)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}